Ray-casting implementations require that the connectivity
between the cells of the dataset to be explicitly computed
and kept in memory. This constitutes a huge obstacle
for obtaining real-time rendering for very large models.
In this paper, we address this problem by introducing
a new implementation of the ray-casting algorithm for irregular
datasets. Our implementation optimizes the memory
usage of past implementations by exploring ray coherence.
The idea is to keep in main memory the information of
the faces traversed by the ray cast through every pixel under
the projection of a visible face. Our results show that
exploring pixel coherence reduces considerably the memory
usage, while keeping the performance of our algorithm
competitive with the fastest previous ones.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
Extraction of Circle of Willis from 2D Magnetic Resonance AngiogramsIDES Editor
Magnetic resonance angiogram is a way to study
cerebrovascular structures. It helps to obtain information
regarding blood flow in a non-invasive fashion. Magnetic
resonance angiograms are examined basically for detection
of vascular pathologies, neurosurgery planning, and vascular
landmark detection. In certain cases it becomes complicated
for the doctors to assess the cerebral vessels or Circle of Willis
from the two-dimensional (2D) brain magnetic resonance
angiograms. In this paper an attempt has been made to extract
the Circle of Willis from 2D magnetic resonance angiograms,
so as to overcome such difficulties. The proposed method preprocesses
the magnetic resonance angiograms and
subsequently extracts the Circle of Willis. The extraction has
been done by color-based segmentation using K-means
clustering algorithm. As the developed method successfully
extracts the vasculature from the brain magnetic resonance
angiograms, therefore it will help the doctors for diagnosis
and serve as a step in the prevention of stroke. The algorithms
are developed on MATLAB 7.6.0 (R2008a) programming
platform.
MULTIPLE RECONSTRUCTION COMPRESSION FRAMEWORK BASED ON PNG IMAGEijcsity
It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, a joint framework based on neural network and scale compression is proposed in this paper. The framework first encodes the incoming PNG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next, we import the intermediate state image into the zooming compressor and repressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in the application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system cannot feel the change of the effect.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
Image Compression and Reconstruction Using Artificial Neural NetworkIRJET Journal
1) The document presents a neural network based method for image compression and reconstruction. An artificial neural network is used to compress image data for storage or transmission and then restore the image when desired.
2) The neural network accepts image data as input, compresses it by generating an internal representation, and then decompresses the data to reconstruct the original image.
3) The performance of the neural network method for image compression and reconstruction is evaluated using standard test images. Results show that it achieves high compression ratios and low distortion while maintaining its ability to generalize and is robust.
Deconstructing SfM-Net architecture and beyond
"SfM-Net, a geometry-aware neural network for motion estimation in videos that decomposes frame-to-frame pixel motion in terms of scene and object depth, camera motion and 3D object rotations and translations. Given a sequence of frames, SfM-Net predicts depth, segmentation, camera and rigid object motions, converts those into a dense frame-to-frame motion field (optical flow), differentiably warps frames in time to match pixels and back-propagates."
Alternative download:
https://www.dropbox.com/s/aezl7ro8sy2xq7j/sfm_net_v2.pdf?dl=0
Secure IoT Systems Monitor Framework using Probabilistic Image EncryptionIJAEMSJORNAL
In recent years, the modeling of human behaviors and patterns of activity for recognition or detection of special events has attracted considerable research interest. Various methods abounding to build intelligent vision systems aimed at understanding the scene and making correct semantic inferences from the observed dynamics of moving targets. Many systems include detection, storage of video information, and human-computer interfaces. Here we present not only an update that expands previous similar surveys but also a emphasis on contextual abnormal detection of human activity , especially in video surveillance applications. The main purpose of this survey is to identify existing methods extensively, and to characterize the literature in a manner that brings to attention key challenges.
AN EFFICIENT M-ARY QIM DATA HIDING ALGORITHM FOR THE APPLICATION TO IMAGE ERR...IJNSA Journal
Methods like edge directed interpolation and projection onto convex sets (POCS) that are widely used for image error concealment to produce better image quality are complex in nature and also time consuming. Moreover, those methods are not suitable for real time error concealment where the decoder may not have sufficient computation power or done in online. In this paper, we propose a data-hiding scheme for error concealment of digital image. Edge direction information of a block is extracted in the encoder and is embedded imperceptibly into the host media using quantization index modulation (QIM), thus reduces work load of the decoder. The system performance in term of fidelity and computational load is improved using M-ary data modulation based on near-orthogonal QIM. The decoder extracts the embedded
features (edge information) and those features are then used for recovery of lost data. Experimental results duly support the effectiveness of the proposed scheme.
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
Extraction of Circle of Willis from 2D Magnetic Resonance AngiogramsIDES Editor
Magnetic resonance angiogram is a way to study
cerebrovascular structures. It helps to obtain information
regarding blood flow in a non-invasive fashion. Magnetic
resonance angiograms are examined basically for detection
of vascular pathologies, neurosurgery planning, and vascular
landmark detection. In certain cases it becomes complicated
for the doctors to assess the cerebral vessels or Circle of Willis
from the two-dimensional (2D) brain magnetic resonance
angiograms. In this paper an attempt has been made to extract
the Circle of Willis from 2D magnetic resonance angiograms,
so as to overcome such difficulties. The proposed method preprocesses
the magnetic resonance angiograms and
subsequently extracts the Circle of Willis. The extraction has
been done by color-based segmentation using K-means
clustering algorithm. As the developed method successfully
extracts the vasculature from the brain magnetic resonance
angiograms, therefore it will help the doctors for diagnosis
and serve as a step in the prevention of stroke. The algorithms
are developed on MATLAB 7.6.0 (R2008a) programming
platform.
MULTIPLE RECONSTRUCTION COMPRESSION FRAMEWORK BASED ON PNG IMAGEijcsity
It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, a joint framework based on neural network and scale compression is proposed in this paper. The framework first encodes the incoming PNG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next, we import the intermediate state image into the zooming compressor and repressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in the application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system cannot feel the change of the effect.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
Image Compression and Reconstruction Using Artificial Neural NetworkIRJET Journal
1) The document presents a neural network based method for image compression and reconstruction. An artificial neural network is used to compress image data for storage or transmission and then restore the image when desired.
2) The neural network accepts image data as input, compresses it by generating an internal representation, and then decompresses the data to reconstruct the original image.
3) The performance of the neural network method for image compression and reconstruction is evaluated using standard test images. Results show that it achieves high compression ratios and low distortion while maintaining its ability to generalize and is robust.
COMPRESSION ALGORITHM SELECTION FOR MULTISPECTRAL MASTCAM IMAGESsipij
The document presents a two-step approach to compressing multispectral images from the Mastcam onboard the Mars rover Curiosity. The approach first applies principal component analysis to compress the 9 image bands into 3 or 6 bands. It then applies various image compression codecs like JPEG, JPEG-2000, X264 and X265 to compress the reduced-band images. Experiments on actual Mastcam images show that combining PCA with X265 achieves the best performance, yielding at least a 5dB improvement in PSNR over JPEG at a 10:1 compression ratio, indicating perceptually lossless compression is possible. The proposed approach may enable more efficient transmission of Mastcam images than the current lossless JPEG method.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
This document describes the development of a 3D convolutional neural network (CNN) model to recognize human activities using moderate computation capabilities. The model is trained on the KTH dataset, which contains activities like walking, running, jogging, handwaving, handclapping, and boxing. The proposed model uses 3D CNN layers and max pooling layers to extract both spatial and temporal features from video frames. Testing achieved an accuracy of 93.33% for activity recognition. The number of model parameters and operations are also calculated to show the model can perform human activity recognition with reasonable computational requirements suitable for devices with moderate capabilities.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
Reversible encrypted data concealment in images by reserving room approachIAEME Publication
The document summarizes a novel method for reversible encrypted data concealment in images. The proposed method reserves room in the original image before encryption using a traditional reversible data hiding algorithm like lifting wavelet transform. This makes it easy for a data hider to reversibly embed data in the encrypted image by simply filling the pre-reserved space. The method is compared to previous "vacating room after encryption" methods and shows it can embed over 10 times as large payloads with the same image quality, as well as achieve better performance in terms of PSNR and MSE. Experiments on test images demonstrate the benefits of the proposed "reserving room before encryption" approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
This document discusses parallelizing graph algorithms on GPUs for optimization. It summarizes previous work on parallel Breadth-First Search (BFS), All Pair Shortest Path (APSP), and Traveling Salesman Problem (TSP) algorithms. It then proposes implementing BFS, APSP, and TSP on GPUs using optimization techniques like reducing data transfers between CPU and GPU and modifying the algorithms to maximize GPU computing power and memory usage. The paper claims this will improve performance and speedup over CPU implementations. It focuses on optimizing graph algorithms for parallel GPU processing to accelerate applications involving large graph analysis and optimization problems.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITIONijaia
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
Volume ray casting algorithms benefit greatly with recent increase of GPU capabilities and power. In this paper,
we present a novel memory efficient ray casting algorithm for unstructured grids completely implemented on GPU
using a recent off-the-shelf nVidia graphics card. Our approach is built upon a recent CPU ray casting algorithm,
called VF-Ray, that considerably reduces the memory footprint while keeping good performance. In addition to
the implementation of VF-Ray in the graphics hardware, we also propose a restructuring in its data structures. As
a result, our algorithm is much faster than the original software version, while using significantly less memory, it
needed only one-half of its previous memory usage. Comparing our GPU implementation to other hardware-based
ray casting algorithms, our approach used between three to ten times less memory. These results made it possible
for our GPU implementation to handle larger datasets on GPU than previous approaches.
Topologically adaptable snakes, or simply T-snakes, are
a standard tool for automatically identifying multiple segments
in an image. This work introduces a novel approach
for controlling the topology of a T-snake. It focuses on the
loops formed by the so-called projected curve which is obtained
at every stage of the snake evolution. The idea is to
make that curve the image of a piecewise linear mapping
of an adequate class. Then, with the help of an additional
structure—the Loop-Tree—it is possible to decide in O(1)
time whether the region enclosed by each loop has already
been explored by the snake. This makes it possible to construct
an enhanced algorithm for evolving T-snakes whose
performance is assessed by means of statistics and examples.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The topologically adaptable snake model, or simply Tsnakes,
is a useful tool for automatically identifying
multiple segments in an image. Recently, in [4], a novel
approach for controlling the topology of a T- snake was
introduced. That approach focuses on the loops formed
by the projected curve which is obtained at every stage
of the snake evolution. The idea is to make that curve the
image of a piecewise linear mapping of an adequate
class. Then, with the help of an additional structure, the
loop-tree, it is possible to decide in O(1) time whether
the region delimited by each loop has already been
explored by the snake. In the original proposal of the
Loop Snakes model, the snake evolution is limited to
contraction and there is only one T-snake that contracts
and splits during evolution. In this paper we generalize
the original model by allowing the contraction as well as
the expansion of several T-Snakes.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
This document discusses an analog signal processing approach for coarse and fine depth estimation using stereo image pairs. It proposes modifications to existing normalized cross correlation (NCC) and sum absolute differences (SAD) stereo correspondence algorithms to reduce computation time. For the NCC algorithm, it suggests using only the diagonal elements of image blocks to compute correlation, reducing computations from 2D to 1D. For hardware implementation, it presents a new imaging architecture with parallel analog and digital systems, where the analog system performs the computationally intensive NCC algorithm on sensor data in real-time to reduce overall processing time compared to digital-only systems. Experimental results show the modified algorithms can achieve faster computation speeds without compromising performance.
1) The document proposes analog signal processing as a solution to reduce computation time for image alignment algorithms that have high computational loads.
2) It modifies the Normalized Cross Correlation (NCC) algorithm for image alignment by only using the diagonal elements of the template and reference image blocks to calculate correlation. This reduces computations compared to using all pixels.
3) A new imaging architecture is proposed that uses an analog processor to implement the modified NCC algorithm in parallel with digital image acquisition, providing faster computation.
COMPRESSION ALGORITHM SELECTION FOR MULTISPECTRAL MASTCAM IMAGESsipij
The document presents a two-step approach to compressing multispectral images from the Mastcam onboard the Mars rover Curiosity. The approach first applies principal component analysis to compress the 9 image bands into 3 or 6 bands. It then applies various image compression codecs like JPEG, JPEG-2000, X264 and X265 to compress the reduced-band images. Experiments on actual Mastcam images show that combining PCA with X265 achieves the best performance, yielding at least a 5dB improvement in PSNR over JPEG at a 10:1 compression ratio, indicating perceptually lossless compression is possible. The proposed approach may enable more efficient transmission of Mastcam images than the current lossless JPEG method.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
This document describes the development of a 3D convolutional neural network (CNN) model to recognize human activities using moderate computation capabilities. The model is trained on the KTH dataset, which contains activities like walking, running, jogging, handwaving, handclapping, and boxing. The proposed model uses 3D CNN layers and max pooling layers to extract both spatial and temporal features from video frames. Testing achieved an accuracy of 93.33% for activity recognition. The number of model parameters and operations are also calculated to show the model can perform human activity recognition with reasonable computational requirements suitable for devices with moderate capabilities.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
Reversible encrypted data concealment in images by reserving room approachIAEME Publication
The document summarizes a novel method for reversible encrypted data concealment in images. The proposed method reserves room in the original image before encryption using a traditional reversible data hiding algorithm like lifting wavelet transform. This makes it easy for a data hider to reversibly embed data in the encrypted image by simply filling the pre-reserved space. The method is compared to previous "vacating room after encryption" methods and shows it can embed over 10 times as large payloads with the same image quality, as well as achieve better performance in terms of PSNR and MSE. Experiments on test images demonstrate the benefits of the proposed "reserving room before encryption" approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
This document discusses parallelizing graph algorithms on GPUs for optimization. It summarizes previous work on parallel Breadth-First Search (BFS), All Pair Shortest Path (APSP), and Traveling Salesman Problem (TSP) algorithms. It then proposes implementing BFS, APSP, and TSP on GPUs using optimization techniques like reducing data transfers between CPU and GPU and modifying the algorithms to maximize GPU computing power and memory usage. The paper claims this will improve performance and speedup over CPU implementations. It focuses on optimizing graph algorithms for parallel GPU processing to accelerate applications involving large graph analysis and optimization problems.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
A common goal of the engineering field of signal processing is to reconstruct a signal from a series of sampling measurements. In general, this task is impossible because there is no way to reconstruct a signal during the times
that the signal is not measured. Nevertheless, with prior knowledge or assumptions about the signal, it turns out to
be possible to perfectly reconstruct a signal from a series of measurements. Over time, engineers have improved their understanding of which assumptions are practical and how they can be generalized. An early breakthrough in signal processing was the Nyquist–Shannon sampling theorem. It states that if the signal's highest frequency is less than half of the sampling rate, then the signal can be reconstructed perfectly. The main idea is that with prior knowledge about constraints on the signal’s frequencies, fewer samples are needed to reconstruct the signal. Sparse sampling (also known as, compressive sampling, or compressed sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions tounder determined linear systems. This is based on the principle that, through optimization, the sparsity of a signal can be exploited to recover it from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible.[1] The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals Possibility
of compressed data acquisition protocols which directly acquire just the important information Sparse sampling (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Sparse sampling is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
TRANSFER LEARNING WITH CONVOLUTIONAL NEURAL NETWORKS FOR IRIS RECOGNITIONijaia
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.
Volume ray casting algorithms benefit greatly with recent increase of GPU capabilities and power. In this paper,
we present a novel memory efficient ray casting algorithm for unstructured grids completely implemented on GPU
using a recent off-the-shelf nVidia graphics card. Our approach is built upon a recent CPU ray casting algorithm,
called VF-Ray, that considerably reduces the memory footprint while keeping good performance. In addition to
the implementation of VF-Ray in the graphics hardware, we also propose a restructuring in its data structures. As
a result, our algorithm is much faster than the original software version, while using significantly less memory, it
needed only one-half of its previous memory usage. Comparing our GPU implementation to other hardware-based
ray casting algorithms, our approach used between three to ten times less memory. These results made it possible
for our GPU implementation to handle larger datasets on GPU than previous approaches.
Topologically adaptable snakes, or simply T-snakes, are
a standard tool for automatically identifying multiple segments
in an image. This work introduces a novel approach
for controlling the topology of a T-snake. It focuses on the
loops formed by the so-called projected curve which is obtained
at every stage of the snake evolution. The idea is to
make that curve the image of a piecewise linear mapping
of an adequate class. Then, with the help of an additional
structure—the Loop-Tree—it is possible to decide in O(1)
time whether the region enclosed by each loop has already
been explored by the snake. This makes it possible to construct
an enhanced algorithm for evolving T-snakes whose
performance is assessed by means of statistics and examples.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The topologically adaptable snake model, or simply Tsnakes,
is a useful tool for automatically identifying
multiple segments in an image. Recently, in [4], a novel
approach for controlling the topology of a T- snake was
introduced. That approach focuses on the loops formed
by the projected curve which is obtained at every stage
of the snake evolution. The idea is to make that curve the
image of a piecewise linear mapping of an adequate
class. Then, with the help of an additional structure, the
loop-tree, it is possible to decide in O(1) time whether
the region delimited by each loop has already been
explored by the snake. In the original proposal of the
Loop Snakes model, the snake evolution is limited to
contraction and there is only one T-snake that contracts
and splits during evolution. In this paper we generalize
the original model by allowing the contraction as well as
the expansion of several T-Snakes.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Analog signal processing approach for coarse and fine depth estimationsipij
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the
market every day. Some of these have very severe Size, Weight and Power constraints whereas other
devices have to handle very high computational loads. Some require both these conditions to be met
simultaneously. Current imaging architectures and digital image processing solutions will not be able to
meet these ever increasing demands. There is a need to develop novel imaging architectures and image
processing solutions to address these requirements. In this work we propose analog signal processing as a
solution to this problem. The analog processor is not suggested as a replacement to a digital processor but
it will be used as an augmentation device which works in parallel with the digital processor, making the
system faster and more efficient. In order to show the merits of analog processing two stereo
correspondence algorithms are implemented. We propose novel modifications to the algorithms and new
imaging architectures which, significantly reduces the computation time
Analog signal processing approach for coarse and fine depth estimationsipij
This document discusses an analog signal processing approach for coarse and fine depth estimation using stereo image pairs. It proposes modifications to existing normalized cross correlation (NCC) and sum absolute differences (SAD) stereo correspondence algorithms to reduce computation time. For the NCC algorithm, it suggests using only the diagonal elements of image blocks to compute correlation, reducing computations from 2D to 1D. For hardware implementation, it presents a new imaging architecture with parallel analog and digital systems, where the analog system performs the computationally intensive NCC algorithm on sensor data in real-time to reduce overall processing time compared to digital-only systems. Experimental results show the modified algorithms can achieve faster computation speeds without compromising performance.
1) The document proposes analog signal processing as a solution to reduce computation time for image alignment algorithms that have high computational loads.
2) It modifies the Normalized Cross Correlation (NCC) algorithm for image alignment by only using the diagonal elements of the template and reference image blocks to calculate correlation. This reduces computations compared to using all pixels.
3) A new imaging architecture is proposed that uses an analog processor to implement the modified NCC algorithm in parallel with digital image acquisition, providing faster computation.
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTIONcscpconf
This document summarizes a research paper that proposes a modified version of Steering Kernel Regression called Median Based Parallel Steering Kernel Regression for improving image reconstruction. The key points are:
1. The proposed algorithm addresses two drawbacks of the original Steering Kernel Regression technique by implementing it in parallel on GPUs and multi-cores to improve computational efficiency, and using a median filter to suppress spurious edges in the output.
2. Experimental results show the proposed algorithm achieves a speedup of 21x using GPUs and 6x using multi-cores compared to serial implementation, while maintaining comparable reconstruction quality as measured by RMSE.
3. The algorithm is implemented iteratively, applying the median filter after each iteration
MEDIAN BASED PARALLEL STEERING KERNEL REGRESSION FOR IMAGE RECONSTRUCTIONcsandit
Image reconstruction is a process of obtaining the original image from corrupted data.Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges(especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used.Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.
Median based parallel steering kernel regression for image reconstructioncsandit
Image reconstruction is a process of obtaining the original image from corrupted data.
Applications of image reconstruction include Computer Tomography, radar imaging, weather
forecasting etc. Recently steering kernel regression method has been applied for image
reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is
computationally intensive. Secondly, output of the algorithm suffers form spurious edges
(especially in case of denoising). We propose a modified version of Steering Kernel Regression
called as Median Based Parallel Steering Kernel Regression Technique. In the proposed
algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The
second problem is addressed by a gradient based suppression in which median filter is used.
Our algorithm gives better output than that of the Steering Kernel Regression. The results are
compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of
21x using GPUs and shown speedup of 6x using multi-cores.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in
edge detection tasks, but their large number of parameters often leads to high memory and energy
costs for implementation on lightweight devices. In this paper, we propose a new architecture, called
Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing
network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge
detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2
datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only
500k parameters, without relying on pre-trained weights.
1. The document proposes a new method for denoising positron flow field images using a generative adversarial network (GAN) with zero-shot learning. This approach can denoise images with small datasets by extracting features from the images themselves.
2. A GAN framework is used, where the generator denoises the images and the discriminator tries to distinguish real from generated images. A new loss function is introduced that combines perceptual and edge losses to preserve image details.
3. Experimental results show the method can reduce noise while retaining key image information, achieving good performance for industrial positron flow field imaging applications with limited data.
Reconstruction of Objects with VSN M.Priscilla - UG Scholar,
B.Nandhini - UG Scholar,
S.Manju - UG Scholar,
S.Shafiqa Shalaysha – UG Scholar,
Christo Ananth - Assistant Professor,
Department of ECE,
Francis Xavier Engineering College, Tirunelveli, India
This document provides a survey of video frame interpolation techniques using deep learning. It discusses benchmark datasets commonly used to evaluate methods, including UCF101, Middlebury, and Vimeo-90K. Two main categories of methods are covered: kernel-based methods that estimate pixel-wise convolution kernels to generate interpolated frames, and flow-based methods that rely on optical flow estimation. Recent works that use adaptive separable convolutions, deformable convolutions, and GANs are summarized. The survey concludes that while kernel-based methods have improved, flow-based techniques still achieve more natural results, and combining frame interpolation with GANs shows promise.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
This document discusses a project to implement image segmentation algorithms in parallel on a RISC processor for real-time processing. The project proposes a new segmentation chain using topological operators like thinning and crest restoration. This chain was tested on medical images and able to process 33 512x512 images per second on a 3.06GHz Pentium IV processor using SSE instructions, demonstrating it can meet real-time constraints. The new chain provides high quality segmentation and is well-suited for medical applications.
A Smart Camera Processing Pipeline for Image Applications Utilizing Marching ...sipij
Image processing in machine vision is a challenging task because often real-time requirements have to be met in these systems. To accelerate the processing tasks in machine vision and to reduce data transfer latencies, new architectures for embedded systems in intelligent cameras are required. Furthermore, innovative processing approaches are necessary to realize these architectures efficiently. Marching Pixels are such a processing scheme, based on Organic Computing principles, and can be applied for example to determine object centroids in binary or gray-scale images. In this paper, we present a processing pipeline for smart camera systems utilizing such Marching Pixel algorithms. It consists of a buffering template for image pre-processing tasks in a FPGA to enhance captured images and an ASIC for the efficient realization of Marching Pixel approaches. The ASIC achieves a speedup of eight for the realization of Marching Pixel algorithms, compared to a common medium performance DSP platform.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Exploring Image Super Resolution TechniquesIRJET Journal
This document discusses image super resolution techniques. It begins by defining super resolution as a technique that reconstructs a high resolution image from low resolution images. It then provides an overview of different super resolution methods including interpolation-based, reconstruction-based, and example-based (machine learning) techniques. The document evaluates state-of-the-art super resolution generative adversarial network (SRGAN) methods and their ability to generate realistic high resolution images from low resolution inputs. It also reviews the history and compares different super resolution techniques.
An Investigation towards Effectiveness in Image Enhancement Process in MPSoC IJECEIAES
The document discusses image enhancement techniques for use in multiprocessor systems-on-chip (MPSoC). It reviews existing image enhancement methods implemented on FPGA and identifies limitations like low accuracy. The paper proposes using advanced bus architectures like AMBA/AXI in MPSoC to improve communication between memory and input medical images for enhancement, reducing heterogeneity effects. It summarizes various image enhancement algorithms that could be implemented on reconfigurable FPGA hardware for use in MPSoC, including brightness control, contrast stretching, histogram equalization, and edge detection techniques.
Thesis on Image compression by Manish MystManish Myst
The document discusses using neural networks for image compression. It describes how previous neural network methods divided images into blocks and achieved limited compression. The proposed method applies edge detection, thresholding, and thinning to images first to reduce their size. It then uses a single-hidden layer feedforward neural network with an adaptive number of hidden neurons based on the image's distinct gray levels. The network is trained to compress the preprocessed image block and reconstruct the original image at the receiving end. This adaptive approach aims to achieve higher compression ratios than previous neural network methods.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
Northern Engraving | Modern Metal Trim, Nameplates and Appliance PanelsNorthern Engraving
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
In our second session, we shall learn all about the main features and fundamentals of UiPath Studio that enable us to use the building blocks for any automation project.
📕 Detailed agenda:
Variables and Datatypes
Workflow Layouts
Arguments
Control Flows and Loops
Conditional Statements
💻 Extra training through UiPath Academy:
Variables, Constants, and Arguments in Studio
Control Flow in Studio
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
1. Memory-Aware and Efficient Ray-Casting Algorithm
Saulo Ribeiro1 , Andr´ Maximo1, Cristiana Bentes2 , Antˆ nio Oliveira1 and Ricardo Farias1
e o
1 2
COPPE - Systems Engineering Program Department of Systems Engineering
Federal University of Rio de Janeiro - Brazil State University of Rio de Janeiro - Brazil
Cidade Universit´ ria - CT - Bloco H - 21941-972
a Rua S˜ o Francisco Xavier, 524 - 20550-900
a
{saulo, andre, oliveira, rfarias}@lcg.ufrj.br cris@eng.uerj.br
Abstract the traveling of a ray through out the mesh is guided by the
connectivity of the cells of the mesh, avoiding the need of
Ray-casting implementations require that the connectiv- sorting the cells. The disadvantage is that cell connectivity
ity between the cells of the dataset to be explicitly com- has to be explicitly computed and kept in memory. In other
puted and kept in memory. This constitutes a huge obsta- words, the amount of memory used by ray-casting meth-
cle for obtaining real-time rendering for very large mod- ods is a huge obstacle for handling very large models.
els. In this paper, we address this problem by introducing Recent previous efforts towards improving ray-casting
a new implementation of the ray-casting algorithm for ir- performance have focused on reducing the execution time
regular datasets. Our implementation optimizes the mem- by exploiting hardware graphics, or GPUs (Graphic Pro-
ory usage of past implementations by exploring ray coher- cessing Unit). This approach, however, only provides in-
ence. The idea is to keep in main memory the information of teractive rendering time when the whole dataset fits into
the faces traversed by the ray cast through every pixel un- the GPU memory. Once the data size exceeds the onboard
der the projection of a visible face. Our results show that memory, expensive transfers from main memory to the GPU
exploring pixel coherence reduces considerably the mem- have great impact on the rendering speed.
ory usage, while keeping the performance of our algorithm Therefore, the problem of rendering large datasets (that
competitive with the fastest previous ones. do not fit in GPU memory) must be addressed by soft-
ware solutions, due to the inherent larger memory capac-
ity [6, 11, 7, 12]. Only a few software solutions, however,
deal with irregular meshes. Garrity [5] proposed the first
1. Introduction method for ray-casting irregular meshes using the connec-
tivity of cells. Bunyk et al. [2] later improved Garrity’s
Direct volume rendering has become a popular technique work providing a faster algorithm. Pina et al. [13] improved
for visualizing volumetric data from sources such as scien- Bunyk approach in: memory consumption; completely han-
tific simulations, analytic functions, and medical scanners, dling degenerate cases; and dealing with both tetrahedral
such as MRI, CT, among others. The main advantage of di- and/or hexahedral meshes. By using new data structures,
rect volume rendering is to allow the investigation of the in- Pina et al. reduced significantly the memory requirements
terior of the data volume, exhibiting its inner structures. of the Bunyk approach, but the algorithms proposed were
Several algorithms and methods have been proposed for still slower than Bunyk.
direct volume rendering, the most popular one is the ray- In this work, we propose a novel ray-casting algorithm
casting algorithm. In this algorithm, a ray is cast through based on Pina et al. approaches, reducing even more the
each pixel of the image from the viewpoint. The trace of memory consumption and improving the execution time.
the ray determines which cells of the volume each ray inter- Our approach is based on the fact that the decision of how
sects. Every pair of intersections is used to compute the cell to store the face information is the key for memory con-
contribution for the pixel color and opacity. The ray stops sumption and execution time. The more information is kept
when it reaches full opacity or when it leaves the volume. in memory, the faster the algorithm computes the next in-
Compared to other direct volume rendering methods, the tersection of the ray. In this work, we tackle this tradeoff
great advantages of ray-casting methods are: the computa- by exploring ray coherence in order to maintain in mem-
tion for each pixel is independent of all other pixels; and ory only the face information of the most recent traversals.
2. Our idea is to use each visible face computed in the pre- entry point by rendering front faces, and then traverse
processing step to guide the creation and destruction of in- through cells using the fragment program by storing the
ternal faces data in memory. Our algorithm, called Visible cells and connectivity graph in textures. Their method, how-
Faces Ray-casting (VF-Ray), obtained consistent and sig- ever, work only on convex unstructured data. Bernardon
nificant gains in memory usage over previous approaches. et al. [1] improve Weiler’s approach, by using Bunyk’s
We also reduced the execution time of Pina et al. approaches algorithm as the basis for the hardware implementation
by making a better use of the cache. When compared to the and depth peeling to correctly render non-convex irregu-
fast and memory expensive approach by Bunyk et al., we lar meshes. Espinha and Celes [3] propose an improvement
obtained comparable performance, spending only from 1/3 in Weiler’s work. They use partial pre-integration and pro-
to 1/6 of the memory. vide interactive modifications of the transfer function. They
The remainder of this paper is organized as follows. In also use an alternative data structure, but as their work is
the next section we discuss direct volume rendering of ir- based on Weiler’s work, the memory consumption is still
regular meshes. Section 3 briefly describes the Bunyk’s and high. Recently, Weiler el al. [17] proposed an improvement
Pina’s approaches. Section 4 describes our ray-casting al- over their previous work, where they deal with the prob-
gorithm and the improvements we made over previous ap- lem of storing the whole dataset in GPU memory by using
proaches. In section 5, we present the results of our most a compressed form, tetrahedral strips.
important experiments. Finally, in section 7, we present our In terms of other direct volume rendering algorithms dif-
conclusions and proposals for future work. ferent from ray-casting, it is important to mention the cell
projection approach. In this approach each polyhedral cell
of the volumetric data is projected onto the screen, avoid-
2. Related Work ing the need of maintaining the data connectivity into the
memory, but requiring the cells to be first sorted in visibil-
Within the research area of accelerating ray-casting
ity ordering and then composed to generate their color and
methods, two main research streams have emerged: soft-
opacity in the final image. In this scope, there are some soft-
ware approaches and graphics hardware approaches.
ware implementations, e.g., [10] and [4], that provide flex-
The very first implementation of the ray-casting algo-
ibility and easy parallelization. The GPU implementations
rithm for rendering irregular meshes was proposed in
of projection algorithms, however, are more popular [16, 9].
software by Garrity in [5]. His approach used the connec-
tivity of cells to compute the entry and exit of each ray. This
work was further improved by Bunyk et al. [2], by com- 3. Previous Approaches
puting for each pixel a list of intersections on visible
faces, and easily determining the correct order of the en- In this section we briefly describe the ray-casting algo-
try points for the ray. The rendering process follows rithm proposed by Bunyk et al. in [2], that we simply call
Garrity’s method, but when a ray exits the mesh, the al- Bunyk, and the ones proposed by Pina et al. in [13], called
gorithm can easily determine in which cell the ray ME-Ray (Memory Efficient Ray-cast) and EME-Ray (En-
will re-enter the mesh. This approach becomes sim- hanced Memory Efficient Ray-cast).
pler and more efficient than Garrity has proposed, however The data structures used by these algorithms to store the
it keeps some large auxiliary data structures. The re- face data guided their memory usage and performance. The
cent work by Pina et al. [13] proposes two new ray-casting face data are stored in a structure that we call face and cor-
algorithms, ME-Raycast and EME-Raycast, that pre- respond to the information about the geometry and the pa-
sented consistent and significant gains in memory usage rameters of the face. The geometry represents the coordi-
and in the correctness of the final image over Bunyk’s ap- nates of the points that define the face. The parameters are
proach. Their algorithms, however, are slower than Bunyk. costly to compute and represent the constants for the equa-
In terms of the acceleration techniques used in this work, tion of the plane defined by the face, that will be used to
ray coherence has been used earlier for accelerating ray compute the intersection between the ray and the face, and
tracing of traditional surface models [14, 15], and later used the parameters are used to interpolate the scalar value in-
in ray-casting for skiping over empty spaces [8]. side the face. The whole face data is the most consuming
With the advent of programmable graphics hardware, data structure in ray-casting.
several volume rendering algorithms have been developed
taking advantage of this feature to achieve high perfor- 3.1. Bunyk
mance. Graphics hardware based solutions usually provide
real-time performance and high quality images. Weiler et In a preprocessing step, all points and tetrahedra of the
al. [16] present a hardware-based ray-casting algorithm input dataset are read, a list of all vertices and a list of cells
based on the work of Garrity. They find the initial ray are created. In addition, for each input tetrahedron, all four
3. of its faces are kept in a list. Bunyk also maintains, for data structures allow them to deal with all degenerate sit-
each vertex, a list of all faces that use that vertex, called uations that Bunyk is not able to handle.
referredBy list. After reading the dataset, Bunyk deter-
mines which faces belong to the visible side of the scene 4. Visible Face Driven Ray-Casting
boundary, the visible faces. The algorithm projects these
visible faces on the screen, creating for each pixel, a list of Comparing the three ray-casting approaches described in
the intersections, that will be used as the entry point of its the previous section, we can observe that the more faces
ray. After the entry points are stored, the actual ray-casting data are kept in memory, the faster the algorithm computes
begins. For every pixel, the first intersection is its entry point the next intersection of the ray. For this reason, Bunyk is
into the data, and each next intersection is obtained by in- still the fastest software implementation of ray-casting.
specting the intersection between the ray and all other faces Our algorithm, called Visible Face Driven Ray-Casting –
of the current cell. Each pair of intersections are used to VF-Ray, addresses the problem of maintaining information
compute the contribution of the cell for the color and opac- about the faces in memory, minimally degrading the render-
ity of the pixel. The searching for the next intersection in ing performance. The basic idea behind VF-Ray is to keep
Bunyk is quite fast, since it is performed among the other in main memory only the faces of the most recent traver-
three faces of the tetrahedron. However, the list of all faces sals. To do so, we explore ray coherence, using the visi-
and the referredBy lists result in a very high memory ble face information to guide the creation and destruction
consumption. of faces in memory.
3.2. ME-Ray 4.1. Exploring Ray Coherence
ME-Ray starts in a preprocessing step, creating a list of VF-Ray is based on ray coherence, which comes from
all vertices, a list of all cells, for each cell the list of cells the fact that the set of faces intersected by two rays, cast
that share a face with it, and the Use Set list for each ver- from neighboring pixels, will contain almost the same faces.
tex. The Use Set lists substitute the referredBy lists Our algorithm takes advantage of ray coherence by keeping
used in Bunyk, each Use Set contains a list of all cells in- in memory only the faces of nearby rays.
cident on the vertex. In the rendering algorithm, ME-Ray One important issue in exploring ray coherence is the de-
creates a list of visible faces, and determine the entry point termination of the set of rays that are likely to have the same
of each ray, in the same way as Bunyk. The algorithm pro- set of intersected faces. We use the visible faces informa-
ceeds looking for the next intersection, by scanning through tion to do so. The set of rays that may reuse the same list
out the neighbor cells. ME-Ray creates on demand a list of faces are the ones that correspond to the set of pixels un-
of faces, as the face is intersected by the ray. Faces that der the projection of a certain visible face. This set of pixels
are never intersected by any ray will not be created. After will be called here visible set. Figure 1 shows a 2D exam-
each pair of intersection is found, the contribution to the fi- ple of this idea. The rays that are cast through the visible
nal color and opacity is computed. ME-Ray can save about face, presented in the figure, tend to intersect the same in-
40% of the memory used by Bunyk, but is slower, as it has ternal faces. Therefore, the internal faces are created and
to create the faces for the first time during the rendering. stored once, for the first ray intersection. After that, they are
reused until all the pixels of the visible set are computed.
3.3. EME-Ray This process is repeated for all visible faces. To guarantee
correctness, all visible faces are ordered in depth order.
EME-Ray was developed as an optimization of ME-Ray
in terms of memory usage. They have similar behavior. screen
However, EME-Ray does not store the faces in memory,
since it was one of the most memory expensive structures in Visible Face
Projection
ME-Ray. Therefore, EME-Ray has to recalculate the faces Internal Faces
parameters for the computation of the intersections every
time a new face is found. With this optimization, EME-Ray
uses only about 1/4 of the memory used by Bunyk. The sig-
nificant reductions in memory usage comes with an increase
in the execution time, EME-Ray usually doubles Bunyk ex-
ecution time. It is also important to notice that ME-Ray and
Figure 1. Visible face rays coherence.
EME-Ray gains over Bunyk are not only in memory us-
age, but also in the correctness of the final image. Their
4. Our results show that exploiting pixel coherence reduces ated and inserted into computedFaces list. Otherwise,
considerably the memory usage, while keeping the VF- the f acej data are loaded from the list. The insertion and
Ray performance competitive with the fastest previous al- loading operations in the list are done in constant time, since
gorithms because the reuse of face data improves cache per- in the cell we store the index for the position of each face
formance. In fact, for all datasets we tested, approximately in the list. The computation of the intersections is done us-
more than half of the faces are created at most two times for ing the face parameters computed when the face is created.
medium size images. For each intersection, the scalar value is interpolated be-
tween the three vertices of each face, which are used in the
4.2. Data structures illumination integral proposed by Max [10].
The data structures used by VF-Ray are similar to that
of EME-Ray: an array of vertices, an array of cells, and For each visible face vf acei do
the Use Set of each vertex. Besides these structures, VF- Project vf acei ; // Determine visible set
Ray also has a list, called computedFaces that holds the For each pixel p of visible set do
Intersect vf acei ; // Interpolate scalar value
internal faces throughout the model. Note that this list is
Do
cleaned up after the ray-casting computation of all pixels of
Find next internal face f acej ; // Intersection
a visible set. In this way, we don’t need to store all the in- If f acej not in computedF aces then
ternal faces, as done by Bunyk and ME-Ray algorithms. Create f acej and Insert in computedF aces;
Furthermore, just like ME-Ray and EME-Ray, our algo- else
rithm can handle any type of convex cell. For tetrahedral Load f acej from computedF aces;
meshes, the intersections are computed between the line de- Ray integrate; // Optical model
fined by the ray path and the plane defined by the three While exist next face f acej ;
vertices of a triangular face. For hexahedral meshes, where End For
each face is quadrangular, the intersection is found by split- Clear computedF aces; // Clear list
ting these quadrangular faces into two triangular faces, and End For
the same calculation is performed for each one.
Figure 2. The pseudo-code of VF-Ray.
4.3. Handling Degeneracies
The degenerate situations, which can be found during
the ray traversal, are treated in the same way that was per-
formed in Pina et al. [13], by investigating the Use set of
each vertex. So, when a ray hits a vertex or an edge, the al- 5. Results
gorithm can determine the next intersection correctly.
In this section we evaluate the performance and memory
4.4. Algorithm usage of VF-Ray when compared to ME-Ray, EME-Ray
and Bunyk. We also show a comparison between VF-Ray
VF-Ray algorithm starts by reading the input dataset and and a cell projection algorithm, called ZSweep [4]. This last
creating three lists: a list of vertices, a list of cells and the experiment was done in order to put our results in perspec-
Use Set list for each vertex. In the rendering process, VF- tive, with respect to other rendering paradigm. The main
Ray creates a list of all visible faces, that are the faces whose idea of ZSweep algorithm is the sweeping of the data with a
normal makes an angle greater than 90o with the viewing plane parallel to the viewing plane XY , towards the pos-
direction. For each visible face, the algorithm follows the itive z direction. The sweeping process is performed by
steps presented in the pseudo-code of Figure 2. ordering the vertices by their increasing z coordinate val-
Initially, the visible face, vf acei , is projected onto the ues, and then retrieving one by one from this data struc-
screen, generating the visible set of vf acei . For each pixel ture. For each vertex swept by the plane sweep, the algo-
p in the visible set, the algorithm first computes the intersec- rithm projects, onto the screen, all faces that are incident to
tion between the ray cast from p and vf acei . After this first it. During face projection, ZSweep stores a list of intersec-
intersection is found, the ray traversal begins, when the al- tions for each pixel and uses delayed compositing. The pix-
gorithm computes the intersections of the ray with the inter- els lists intersections are composed and the lists are flushed
nal faces. The next face intersected, f acej , is found on an- each time the plane reaches the target-z. The target-z is the
other face of the current cell. If f acej was not created be- maximum z-coordinate among the vertices adjacent to the
fore (this is the first intersection in f acej ), the face is cre- vertex intersected by the sweeping plane.
5. 5.1. Testing Configuration ME-Ray uses from 1.5 to 6 times more memory than VF-
Ray. For EME-Ray, we notice an interesting result. Al-
The VF-Ray algorithm was written in C++ ANSI with- though EME-Ray does not store the faces in memory, for
out using any particular graphics libraries. Our experiments high resolution images, bigger than 1024 × 1024, EME-
were conducted on a Intel Pentium IV 3.6 GHz with 2 GB Ray uses more memory than VF-Ray. This result can be
RAM and 1MB of L2 cache, running Linux Fedora Core 5. explained by the fact that EME-Ray, just like Bunyk and
We have used four different tetrahedral datasets: Blunt ME-Ray, maintains for each pixel a list containing the entry
Fin, SPX Liquid Oxygen Post, and Delta Wing. Table 1 faces for the pixel. The number of lists increases with the in-
shows the number of vertices, faces, boundary faces and crease in image resolution. VF-Ray, on the other hand, does
cells for each dataset. We varied the image sizes, from not require this kind of list, since it computes the entry point
512 × 512 to 8192 × 8192 pixels. The models rendered with of each ray on demand, for each visible face.
VF-Ray are shown in Figures 3 to 6. The images generated When compared to ZSweep, we can observe that except
by VF-Ray, ME-Ray, and EME-Ray are identical, and bet- for SPX with a 512 × 512 image, VF-Ray uses much less
ter than the images generated by Bunyk. There are two rea- memory than ZSweep. For the higher resolution images,
sons for this difference: VF-Ray, ME-Ray, and EME-Ray ZSweep can use 8 or 9 times more memory than VF-Ray.
use double precision variables to compute ray-faces inter- To render SPX with a 8192 × 8192 image, for example,
sections, while Bunyk used float precision, causing artifacts ZSweep spends about 2GB of memory. The ZSweep mem-
in the images, and Bunyk does not handle correctly all de- ory consumption is directly proportional to the final image
generate cases, generating some incorret pixels. resolution, since it creates pixels lists to store the intersec-
tions found for each pixel.
Dataset # Verts # Faces # Tets # Boundary
Blunt 41 K 381 K 187 K 13 K 5.3. Rendering Performance
SPX 149 K 1.6 M 827 K 44 K
Table 3 shows the rendering time (in seconds) of VF-Ray
Post 109 K 1M 513 K 27 K
for Blunt, SPX, Post and Delta, and the relative timing re-
Delta 211 K 2M 1M 41 K
sults of ME-Ray, EME-Ray, Bunyk and ZSweep when com-
Table 1. Dataset sizes. pared to VF-Ray execution. Just like the previous table, the
percentages presented in this table correspond to the ratio
of the other algorithms execution time over VF-Ray execu-
tion time. The time results correspond to the rendering of
5.2. Memory Consumption one frame and do not include preprocessing time.
As we can observe in Table 3, VF-Ray outperforms ME-
Table 2 shows the memory used by VF-Ray to render Ray, EME-Ray and ZSweep for all datasets and all image
one frame (in MBytes) for Blunt, SPX, Post and Delta, and resolutions. For Blunt, Post and Delta, VF-Ray is about 3
the relative memory usage results of ME-Ray, EME-Ray, times faster than EME-Ray and ZSweep, and uses about
Bunyk and ZSweep when compared to VF-Ray usage. The 3/4 of the time used by ME-Ray to render the image. For
percentages presented in this table correspond to the ratio SPX, the gains of VF-Ray against EME-Ray and ZSweep
of the other algorithms memory usage over VF-Ray mem- are smaller, but still high. When compared to Bunyk, for
ory consumption. In other words, we consider VF-Ray re- Blunt and Delta, VF-Ray presents almost the same execu-
sults as 100% and are presenting how much the other algo- tion time, since the difference is only about 10%. For SPX
rithms increase or decrease this result. and Post, VF-Ray is only about 22% slower than Bunyk.
As we can observe in Table 2, in most of the cases, Observing the increase in the rendering time of VF-Ray
VF-Ray uses less memory than the three other algorithms, for different image resolutions, we notice that as the im-
and its memory usage increases linearly with the resolution. age resolution increases, the rendering time increases in the
When compared to Bunyk, VF-Ray uses from 1/3 to 1/6 of same proportion. The gains over the other algorithms are al-
the memory spent by Bunyk. These gains are impressive. most the same for different resolutions.
For Blunt, for example, VF-Ray renders a 4096 × 4096 im-
age using the same amount of memory that Bunyk uses to 6. Discussion
render a 512 × 512 image. For the largest dataset, Delta,
Bunyk uses more memory to render a 512 × 512 image than VF-Ray had obtained consistent and significant gains in
VF-Ray uses to render a 8192 × 8192 image. memory usage over Bunyk, providing almost the same per-
When compared with the two memory-aware ray-casting formance (the difference on their execution time is only
algorithms, ME-Ray and EME-Ray, we can observe that about 16%). As the image resolution increases, our gains
6. Dataset Resolution Memory (MB) ME-Ray EME-Ray Bunyk ZSweep
512 × 512 14 404% 166% 528% 208%
1024 × 1024 30 240% 129% 297% 177%
Blunt 2048 × 2048 39 333% 251% 377% 369%
4096 × 4096 75 495% 451% 517% 689%
8192 × 8192 219 610% 655% 617% 917%
512 × 512 96 215% 74% 299% 87%
1024 × 1024 99 234% 88% 305% 107%
SPX 2048 × 2148 108 271% 141% 337% 188%
4096 × 4096 144 383% 284% 428% 407%
8192 × 8192 288 533% 498% 569% 711%
512 × 512 63 165% 74% 290% 97%
1024 × 1024 66 203% 95% 301% 129%
Post 2048 × 2048 74 281% 172% 353% 238%
4096 × 4096 110 424% 349% 470% 499%
8192 × 8192 256 601% 560% 603% 800%
512 × 512 116 167% 74% 303% 97%
1024 × 1024 119 200% 84% 308% 116%
Delta 2048 × 2048 129 246% 124% 330% 177%
4096 × 4096 165 346% 247% 403% 375%
8192 × 8192 307 500% 467% 534% 704%
Table 2. Memory usage results for VF-Ray compared to ME-Ray, EME-Ray, Bunyk and ZSweep.
in memory usage over Bunyk are even more pronounced, execution time come from the good cache performance of
but the difference in the rendering time remains the same. VF-Ray. We have made some experiments to evaluate the
For 8192 × 8192 images, Bunyk uses about 6 times more cache misses of VF-Ray and ME-Ray and obtained for VF-
memory than VF-Ray with a performance difference of Ray a cache miss ratio very low, near 0%, while the cache
only about 18%. In our experiments, however, we are only miss ratio of ME-Ray is about 1.9% for modest size im-
comparing executions where the whole dataset fits in main ages. This result was obtained because our algorithm reuse
memory for all algorithms. Our experimental platform has the faces for the rays in the same visible face. Given the fact
2 GB of main memory that can store all the data structures that rays are shot one after the other, the closer the neigh-
used by our workload. As the memory usage increases, the boring rays are to each other, the higher the probability is
rendering will need to use the virtual memory mechanisms that they reuse the cached data.
of the operating system, which would have great influence The comparison of VF-Ray with the cell projection ap-
on the overall execution time. proach, implemented by ZSweep, showed that VF-Ray is
The small increase in the execution time, when compared faster and spends less memory than ZSweep. The gains
to Bunyk, comes from the fact that Bunyk computes all in- are huge. For the Delta dataset, ZSweep uses 7 times more
ternal faces in the preprocessing step, before the ray-casting memory, running about 3.5 times slower than VF-Ray. The
loop, while VF-Ray does it on demand, as the face is inter- pixel list maintained by the ZSweep approach increases
sected for the first time. In addition, in VF-Ray, if a face is while a target-Z was not reached. As this list grows, the
intersected by rays of two different visible faces, it has its insertion is more expensive, since the pixel list must be or-
data computed twice. dered. On the other hand, the data structures on the VF-Ray
The comparison of VF-Ray with ME-Ray and EME- algorithm does not depend on ordering. The difference in
Ray provided interesting results. ME-Ray and EME-Ray memory usage of ZSweep is due to the increase in these
are memory-aware algorithms designed to reduce the great pixels lists. For ZSweep, as the image size increases, each
memory usage of Bunyk. VF-Ray was designed to reduce face projected will insert intersection units into more pixel
ME-Ray memory usage, close to the usage of EME-Ray, but lists. If the target-Z is not appropriate, the pixels lists will
running much faster than EME-Ray. Our results showed, increase considerably.
however, that for high resolution images, our algorithm is In terms of the dataset characteristics, the performance
not only faster than ME-Ray but uses less memory than of VF-Ray increases as the number of pixels in each vis-
EME-Ray. The gains of VF-Ray over EME-Ray in mem- ible face increases. Datasets with a small number of faces
ory usage come from the elimination of the lists of entry have a greater amount of pixels per visible face. Therefore,
faces for each pixel. The gains of VF-Ray over ME-Ray in datasets, like Blunt, that has a small number of faces, tend
7. Dataset Resolution Time (sec) ME-Ray EME-Ray Bunyk ZSweep
512 × 512 1.9 139% 296% 105% 253%
1024 × 1024 7.0 143% 317% 94% 431%
Blunt 2048 × 2048 27.2 146% 337% 88% 481%
4096 × 4096 107.4 147% 328% 88% 516%
8192 × 8192 426.7 154% 341% 91% 382%
512 × 512 4.1 111% 161% 76% 287%
1024 × 1024 13.0 103% 203% 81% 202%
SPX 2048 × 2048 46.6 103% 225% 83% 219%
4096 × 4096 177.1 105% 234% 82% 226%
8192 × 8192 696.3 105% 238% 81% 260%
512 × 512 5.0 138% 251% 80% 201%
1024 × 1024 19.5 136% 254% 76% 167%
Post 2048 × 2048 78.6 133% 258% 74% 194%
4096 × 4096 309.9 135% 257% 74% 227%
8192 × 8192 1246.3 135% 263% 72% 246%
512 × 512 3.1 130% 242% 91% 465%
1024 × 1024 11.2 122% 270% 88% 271%
Delta 2048 × 2048 41.9 121% 280% 87% 312%
4096 × 4096 162.7 120% 290% 84% 341%
8192 × 8192 640.3 123% 290% 83% 369%
Table 3. Time results for VF-Ray compared to ME-Ray, EME-Ray, Bunyk and ZSweep.
to present better performance results for VF-Ray.
Finally, it is important to keep in mind that the reduc-
tions in memory usage will allow the implementation of the
algorithm in the graphics hardware. Furthermore, these re-
ductions will also allow the use of double precision in the
parameters to calculate the intersection between a ray and a
face, raising the chance to obtain more precise images.
Figure 4. SPX image generated by VF-Ray.
dral or hexahedral datasets, including non-convex and dis-
connected meshes even with holes.
Our idea was to explore ray coherence in order to reduce
the memory requirements for the storing of faces data while
achieving a good cache performance. We compared VF-Ray
Figure 3. Blunt image generated by VF-Ray.
memory usage and performance with previous ray-casting
approaches, and with a cell projection algorithm. When
compared to the fastest and memory expensive approach, by
Bunyk, VF-Ray obtained comparable performance, spend-
7. Conclusions ing only from 1/3 to 1/6 of the memory. When compared
to the memory-aware Pina et al algorithms, VF-Ray ob-
In this work, we have proposed a novel memory-aware tained consistent and significant gains in memory usage and
and efficient software ray-casting algorithm for volume ren- in execution time. When compared to the cell projection ap-
dering, called VF-Ray. The algorithm is suitable for tetrahe- proach, VF-Ray also reduced significantly the memory con-
8. [2] P. Bunyk, A. Kaufman, and C. Silva. Simple, fast, and ro-
bust ray casting of irregular grids. Advances in Volume Visu-
alization, ACM SIGGRAPH, 1(24), July 1998.
[3] R. Espinha and W. Celes. High-quality hardware-based ray-
casting volume rendering using partial pre-integration. In
SIBGRAPI ’05: Proc. of the XVIII Brazilian Symposium on
Computer Graphics and Image Processing, October 2005.
[4] R. Farias, J. Mitchell, and C. Silva. Zsweep: An efficient and
exact projection algorithm for unstructured volume render-
ing. In 2000 Volume Visualization Symposium, pages 91 –
99, October 2000.
[5] M. P. Garrity. Raytracing irregular volume data. In VVS ’90:
Proceedings of the 1990 workshop on Volume visualization,
pages 35–40. ACM Press, 1990.
Figure 5. Post image generated by VF-Ray. [6] L. Hong and A. Kaufman. Accelerated ray-casting for curvi-
linear volumes. In VIS ’98: Proceedings of the conference
on Visualization ’98, pages 247–253, 1998.
[7] K. Koyamada. Fast ray-casting for irregular volumes. In
ISHPC ’00: Proc. of the Third International Symposium on
High Performance Computing, pages 557–572, 2000.
[8] S. Lakare and A. Kaufman. Light weight space leaping us-
ing ray coherence. In Proc. IEEE Visualization 2004, pages
19–26, 2004.
[9] R. Marroquim, A. Maximo, R. Farias, and C. Esperanca.
Gpu-based cell projection for interactive volume rendering.
In SIBGRAPI ’06: Proc. of the Brazilian Symposium on
Computer Graphics and Image Processing, October 2006.
[10] N. Max. Optical models for direct volume rendering.
IEEE Transactions on Visualization and Computer Graph-
ics, 1(2):99–108, 1995.
[11] M. Meibner, J. Huang, D. Bartz, K. Mueller, and R. Craw-
Figure 6. Delta image generated by VF-Ray. fis. A practical evaluation of popular volume rendering algo-
rithms. In VVS ’00: Proceedings of the 2000 IEEE sympo-
sium on Volume visualization, pages 81–90, 2000.
sumption and the execution time. [12] A. Neubauer, L. Mroz, H. Hauser, and R. Wegenkittl. Cell-
The reductions in memory usage makes VF-Ray suitable based first-hit ray casting. In VISSYM ’02: Proceedings of the
for using double precision in the parameters to calculate the symposium on Data Visualisation 2002, pages 77–ff, 2002.
intersection between a ray and a face. Using double preci- [13] A. Pina, C. Bentes, and R. Farias. Memory efficient and ro-
sion avoids the artifacts that appear when float precision is bust software implementation of the raycast algorithm. In
used. We conclude that VF-Ray is an efficient ray-casting WSCG’07: The 15th Int. Conf. in Central Europe on Com-
puter Graphics, Visualization and Computer Vision, 2007.
algorithm that allows the in-core rendering of big datasets.
[14] A. Reshetov, A. Soupikov, and J. Hurley. Multi-level ray
As future work, we intend to optimize VF-Ray by keeping
tracing algorithm. In SIGGRAPH ’05: ACM SIGGRAPH
face data for neighboring visible faces, and to parallelize
2005 Papers, pages 1176–1185, 2005.
VF-Ray in order to run on a cluster of PCs [15] I. Wald, P. Slusallek, C. Benthin, and M. Wagner. nterac-
tive rendering with coherent ray tracing. In Eurographics 01
8. Acknowledgments Proceedings, pages 153–164, 2001.
[16] M. Weiler, M. Kraus, M. Merz, and T. Ertl. Hardware-based
We acknowledge the grant of the first and second authors ray casting for tetrahedral meshes. In Proceedings of the 14th
provided by Brazilian agencies CAPES and CNPq. IEEE conference on Visualization ’03, pages 333–340, 2003.
[17] M. Weiler, P. Mall´ n, M. Kraus, and T. Ertl. Texture-encoded
o
tetrahedral strips. In 2004 IEEE Symposium on Volume Visu-
References alization and Graphics (VolVis 2004), pages 71–78, October
2004.
[1] F. Bernardon, C. Pagot, J. Comba, and C. Silva. Gpu-based
tiled ray casting using depth peeling. Journal of Graphics
Tools, To appear.