Poster: AVSS 2012
•
0 likes•227 views
1) The document proposes a background subtraction technique called multi-hypothesis mixture-of-Gaussians (MH-MOG) that generates multiple detection hypotheses from a single background model to achieve superior foreground detection quality for real-time video analytics. 2) MH-MOG maintains a single background pixel model using a perception-aware mixture-of-Gaussians and generates two independent detection hypotheses: a perception-inspired hypothesis and a probabilistic hypothesis. 3) It computes confidence levels for each hypothesis and uses this information in a detection algorithm to produce a high quality foreground mask by maximizing the complementary strengths of the two hypotheses, outperforming conventional techniques that use multiple background models.
Report
Share
Report
Share
Download to read offline
![Background Subtraction for Real-time Video Analytics Based on
Multi-hypothesis Mixture-of-Gaussians
Mahfuzul Haque and Manzur Murshed
Gippsland School of Information Technology, Monash University, Victoria 3842, Australia
Email: {Mahfuzul.Haque, Manzur.Murshed}@monash.edu
1 Abstract
Robust background subtraction (BS) is essential for high quality foreground detection in most video analytics systems. Recent
BS techniques achieve superior detection quality mostly by exploiting the complementary strengths of multiple background
models. Consequently, these techniques fail to meet the operational requirements of real-time video analytics. The proposed
BS technique, named multi-hypothesis mixture-of-Gaussians (MH-MOG), maintains a single background model based on
perception-aware mixture-of-Gaussians and then, generates multiple detection hypotheses with different processing bases.
Finally, only during the detection stage, the complementary strengths of the hypotheses are exploited to achieve superior
detection quality without significant computational overhead.
2 Dynamic Background Subtraction
3 Multiple Detection Hypotheses for Superior Detection Quality
Detection Decision
Detection Decision
Background
Model
Video Frame
Hypothesis1
Hypothesis2
…
Hypothesisn
Model1
Model2
…
Modeln
Foreground Mask
Hypothesis1
Hypothesis2
Hypothesisn
Model
Proposed
Conventional
Dynamic background subtraction is an essential precursor in
most video analytics systems for moving foreground detection.
The quality of foreground detection directly impacts the
performance of subsequent processing tasks.
…
To achieve superior detection quality conventional approaches use the complementary strengths of multiple
detection hypotheses that are originated from different background models while the proposed technique
uses a single underlying background model to generate complementary detection hypotheses.
4 The Proposed Background Subtraction Technique (MH-MOG)
MH-MOG
Background
Model
Perception Inspired
Detection Hypothesis
Confidence Level for
Detection Hypothesis
Detection Algorithm
Probabilistic
Detection Hypothesis
Confidence Level for
Detection Hypothesis
High Quality
Foreground Mask
Incoming
Video Stream
In the proposed background subtraction technique, a single background model is maintained based on observed video frames. Then based on this background model two
independent detection hypotheses (e.g., perception inspired and probabilistic) are generated. For both hypotheses, associated confidence levels are computed based on
spatial detection results in the corresponding hypothesis space. Finally, all these information is used by the detection algorithm to produce high quality foreground mask by
maximising the complementary strengths of both hypotheses [1].
6 Perception inspired detection hypothesis
5 Background Modelling
Observed intensity value: m
Mean: µ
Standard deviation: σ
Weight: ω
P(x)
Intensity
The background of the operating environment is modelled at
pixel-level by maintaining at most N observed intensity values
(m1, m2, …, mN). For each sample, associated Gaussian
variables (µ, σ, and ω) are maintained to determine the order
of the samples based on observation frequency.
8 Experiments
0
m1
m2
Intensity
m3
255
A confidence interval is determined for each believed-to-bebackground intensity value based on the characteristics of
human visual system in perceiving noticeable intensity
deviation from background (Weber’s Law). Observed
intensity values are classified as background based on their
membership in any background confidence interval [2].
9 Quantitative Evaluation
Quantitative comparison: This figure shows overall (ALL),
dataset-wise (PETS, WF, UCF, IBM, CAV, VSSN), and sequenceclass-wise (SR, MM, LC) performance comparisons.
Unlike perception inspired hypothesis, no subset
of samples is chosen as background for intensity
comparison. Rather a probabilistic formulation
involving all Gaussian components is used [3] for
background/foreground
classification.
This
hypothesis shows higher foreground sensitivity
and thus recovers missing foreground regions
due to intensity thresholding by the perception
inspired hypothesis.
10 Visual Comparison
First Frame
More than 50 test sequences
were selected from eight
different datasets including
PETS,
Wallflower,
IBM,
VSSN06, CAVIAR, and UCF
and categorised in following
classes
based
on
the
characteristics of the operating
environments: low contrast
foreground (LC), shadows and
reflections (SR), multi-modal
background
(MM),
indoor
(INDOOR),
and
outdoor
(OUTDOOR).
7 Probabilistic detection hypothesis
Test Frame
Ground Truth MOG (S&G)
MOG (Lee)
ViBe
MH-MOG
MOG (S&G) – TPAMI 2000, MOG (Lee) – TPAMI 2005, ViBe – TIP 2011, and MH-MOG – Proposed.
[1] M. Haque and M. Murshed, Background Subtraction for Real-time Video Analytics Based on Multi-hypothesis Mixture-of-Gaussians, IEEE International
Conference on Advanced Video and Signal Based Surveillance (AVSS), Beijing, China, 2012.
[2] M. Haque and M. Murshed, Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed, IEEE International
Workshop on Advances in Automated Multimedia Surveillance for Public Safety, Melbourne, Australia, 2012.
[3] D.-S. Lee. Effective Gaussian mixture learning for video background subtraction, IEEE TPAMI, 27(5):827– 832, 2005.
The images shown in the header has been taken from http://www.informationliberation.com](https://image.slidesharecdn.com/poster-avss-2012-131229012645-phpapp02/85/Poster-AVSS-2012-1-320.jpg)
Recommended
Adaptive block-based pixel value differencing steganography
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The block’s median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Poster: EII Winter School 2007
1. The document proposes a real-time background estimation method for video surveillance using a modified Gaussian Mixture Model (GMM) that estimates the background frame from input video frames rather than classifying pixels as foreground or background.
2. The proposed method models each pixel as a mixture of K Gaussian distributions learned over time and estimates the background, which is then subtracted from the current frame to extract the foreground.
3. Experimental results on real and synthesized videos show the proposed background estimation method performs better foreground extraction in dynamic scenes compared to existing GMM-based methods.
Robust foreground modelling to segment and detect multiple moving objects in ...
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
Deep Learning for Structure-from-Motion (SfM)
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
Steganography Using Reversible Texture Synthesis
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Iee egold2010 presentazione_finale_veracini
The document proposes an anomaly detection scheme for hyperspectral images based on a non-Gaussian mixture model using a Student's t-distribution. It estimates the background probability density function using a Bayesian approach that models each pixel as a mixture of Student's t distributions. The anomaly detection strategy then applies a generalized likelihood ratio test. Experimental results on real hyperspectral data show the proposed Bayesian Student's t mixture model can reliably estimate the background distribution and effectively detect anomalous objects, outperforming a Gaussian mixture model approach.
Paper Explained: One Pixel Attack for Fooling Deep Neural Networks
Read more: https://devanshverma425.medium.com/what-should-we-learn-from-the-one-pixel-attack-a67c9a33e2a4
Abstract—Recent research has revealed that the output of Deep
Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a blackbox attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate lowcost adversarial attacks against neural networks for evaluating robustness.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It’s a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let’s connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
Deep learning for image super resolution
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
Recommended
Adaptive block-based pixel value differencing steganography
Steganography is the science of hiding secure data in digital carriers such as images and videos. Pixel value differencing
(PVD) steganography algorithms embed data into images depending on pixel neighborhood differences. We have pro-
posed PVD scheme for embedding secure data into digital images. The image is divided into non-overlapping 33 blocks.
The block’s median pixel is used as a reference for calculating pixel differences. The distance between the minimum and
maximum differences are fine tuned for spreading the secure data on a wide range of image regions with high-intensity
fluctuations. The embedding procedure embeds secure data into the content regions with edges and intensity transitions.
Texture images provide higher embedding size compared with regular images. The results showed that the proposed
algorithm is successfully able to avoid smooth regions in the embedding process. In addition, the proposed algorithm
shows better embedding quality compared with the state of the art PVD approaches especially with low-embedding rates.
Poster: EII Winter School 2007
1. The document proposes a real-time background estimation method for video surveillance using a modified Gaussian Mixture Model (GMM) that estimates the background frame from input video frames rather than classifying pixels as foreground or background.
2. The proposed method models each pixel as a mixture of K Gaussian distributions learned over time and estimates the background, which is then subtracted from the current frame to extract the foreground.
3. Experimental results on real and synthesized videos show the proposed background estimation method performs better foreground extraction in dynamic scenes compared to existing GMM-based methods.
Robust foreground modelling to segment and detect multiple moving objects in ...
This document summarizes a research paper that proposes a robust foreground modeling method to segment and detect multiple moving objects in videos. The proposed method uses a running average technique to model the background and subtract it from video frames to detect foreground objects. Morphological operations like dilation and erosion are applied to reduce noise and merge connected regions. Convex hull processing is also used to define object boundaries more clearly. The method was tested on standard video datasets and achieved better performance than other techniques in segmenting objects under various challenging conditions like illumination changes and occlusion. Experimental results demonstrated high precision, recall and specificity based on comparisons with ground truth data.
Deep Learning for Structure-from-Motion (SfM)
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
Steganography Using Reversible Texture Synthesis
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Iee egold2010 presentazione_finale_veracini
The document proposes an anomaly detection scheme for hyperspectral images based on a non-Gaussian mixture model using a Student's t-distribution. It estimates the background probability density function using a Bayesian approach that models each pixel as a mixture of Student's t distributions. The anomaly detection strategy then applies a generalized likelihood ratio test. Experimental results on real hyperspectral data show the proposed Bayesian Student's t mixture model can reliably estimate the background distribution and effectively detect anomalous objects, outperforming a Gaussian mixture model approach.
Paper Explained: One Pixel Attack for Fooling Deep Neural Networks
Read more: https://devanshverma425.medium.com/what-should-we-learn-from-the-one-pixel-attack-a67c9a33e2a4
Abstract—Recent research has revealed that the output of Deep
Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input vector. In this paper, we analyze an attack in an extremely limited scenario where only one pixel can be modified. For that we propose a novel method for generating one-pixel adversarial perturbations based on differential evolution (DE). It requires less adversarial information (a blackbox attack) and can fool more types of networks due to the inherent features of DE. The results show that 67.97% of the natural images in Kaggle CIFAR-10 test dataset and 16.04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74.03% and 22.91% confidence on average. We also show the same vulnerability on the original CIFAR-10 dataset. Thus, the proposed attack explores a different take on adversarial machine learning in an extreme limited scenario, showing that current DNNs are also vulnerable to such low dimension attacks. Besides, we also illustrate an important application of DE (or broadly speaking, evolutionary computation) in the domain of adversarial machine learning: creating tools that can effectively generate lowcost adversarial attacks against neural networks for evaluating robustness.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It’s a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let’s connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
Deep learning for image super resolution
Using Deep Convolutional Networks, the machine can learn end-to-end mapping between the low/high-resolution images. Unlike traditional methods, this method jointly optimizes all the layers of the image. A light-weight CNN structure is used, which is simple to implement and provides formidable trade-off from the existential methods.
SVM-KNN Hybrid Method for MR Image
1. The document presents a hybrid SVM-KNN classification method for classifying MRI brain images to detect tumors. It combines support vector machines (SVM) and K-nearest neighbors (KNN) to leverage the strengths of both algorithms.
2. The algorithm first uses KNN to classify an MRI image based on its similarity to labeled training images. If KNN is uncertain or confused, it then uses SVM to classify the image by finding the optimal separating hyperplane between tumor and non-tumor classes.
3. The authors implemented this hybrid SVM-KNN algorithm in MATLAB and were able to successfully classify test MRI images as depicting a normal brain or an abnormal brain with a tumor.
I0341042048
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.
A Framework of Secured and Bio-Inspired Image Steganography Using Chaotic Enc...
This document proposes a novel secured image steganography technique called CEGAO that uses chaotic encryption and genetic algorithm optimization. It encrypts a secret image using a logistic map before embedding it in the least significant bits of cover images. To extract the secret image, the recipient decrypts and retrieves the secret image blocks from the stego image using the same logistic map. The technique achieves PSNR values up to 48.55 dB and SSIM values over 0.99, outperforming conventional LSB techniques. While performance is encouraging, the authors aim to further improve resistance against statistical attacks and optimize other aspects of the algorithm.
Pixel Recursive Super Resolution. Google Brain
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
Fcv poster efros
The document proposes an object detection method that combines the effectiveness of discriminative detectors with the explicit correspondence of nearest-neighbor approaches. It trains a separate linear SVM classifier for each exemplar in the training set, where each detector is defined by a single positive instance and millions of negatives. While each detector is specific to its exemplar, the paper finds that an ensemble of these Exemplar-SVMs offers good generalization. Their method achieves performance on par with more complex latent part-based models, with only a modest increase in computational cost.
Target Detection by Fuzzy Gustafson-Kessel Algorithm
Many commercially available radar systems offer a range of filter options but the problem of clutter rejection for target detection is still present in a number of situations. Rejection of clutter and detection of targets from radar captured data is a challenging task. Raw data captured by radar are not always scaled. A normalization technique has been proposed which transforms the radar captured data into 8 bit. As 8 bit data is easy to analyze and visualize. A modification on Fuzzy c-means has been done by developing Fuzzy Gustafson–Kessel (FGK) algorithm and the result shows robustness of this proposed method.
M017427985
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.
proposal_pura
This document proposes a linear recurrent convolutional neural network model for segment-based multiple object tracking in video. The model takes images as input and uses a CNN to classify superpixels, then performs segmentation and uses nonlinear NNs and a linear recurrent tracker layer to match segments over time. The objectives are to improve the tracker layer efficiency by modifying the matrix inverse and determine parameters for the model. Evaluation will use a dataset with ground truth segmentation and optical flow to train and compare to state-of-the-art methods.
Matrioska tracking keypoints in real-time
Title PDF: MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning | ICIAP 2013
In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present numerous quantitative results on a number of challenging sequences in which the target object goes through changes of pose, scale and illumination.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
An improved particle filter tracking
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
The super resolution technology 2016
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
2019 cvpr paper_overview
Statistics and Visualization of acceptance rate, main keyword for the main Computer Vision conference (CVPR)
20 CVPR Paper Review
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Distance-based bias in model-directed optimization of additively decomposable...
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Arp zmp
This document proposes an adaptive rood pattern search (ARPS) algorithm for fast block matching motion estimation. ARPS uses adjustable search patterns centered around a predicted motion vector value to quickly locate the minimum matching error point. It employs a checking bit-map and zero-motion prejudgment to avoid duplicate computations and benefit sequences with small motion. Experimental results show ARPS achieves 94-447x speedup over full search with less than 0.12dB drop in PSNR quality compared to full search for most sequences.
A Deep Belief Network Approach to Learning Depth from Optical Flow
The document describes using a deep belief network to learn depth from optical flow in videos. A biologically inspired model is used to generate motion features from video frames. These motion features are then used as input to a deep neural network that is trained to predict depth maps. The network is initialized using unsupervised pre-training of restricted Boltzmann machines and then fine-tuned with supervised backpropagation. Computer-generated graphics are used to obtain labeled training data of video frames paired with ground truth depth maps. The results show improved depth prediction over standard classifiers, demonstrating the potential of unsupervised feature learning for computer vision tasks.
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Anal...
Paper: https://arxiv.org/abs/2004.00448
Github: https://github.com/clovaai/cutblur
Youtube: https://youtu.be/MQR5mnsCvbM
IEEE 2014 Matlab Projects
List of IEEE Matlab Projects. It Contains the IEEE Projects in the Language Matlab with ieee publication year 2014
Poster: ICPR 2008
This paper presents a technique to improve Gaussian mixture models for robust object detection by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more background models. The proposed method eliminates drawbacks of poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and instability with varying operating environments. Quantitative and qualitative evaluations on test video sequences show the proposed technique achieves lower error rates and better visual results compared to existing methods.
Hyperspectral unmixing using novel conversion model.ppt
The document presents a novel hyperspectral unmixing approach called uccm-SVM that converts the abundance quantification problem into a classification problem using support vector machines. The approach is tested on both simulated and real hyperspectral images and is shown to outperform traditional mean-based techniques like FCLS in terms of accuracy while having lower computational costs for smaller training set sizes. Future work to improve the method includes enhancing performance while reducing computation for larger training sets.
More Related Content
What's hot
SVM-KNN Hybrid Method for MR Image
1. The document presents a hybrid SVM-KNN classification method for classifying MRI brain images to detect tumors. It combines support vector machines (SVM) and K-nearest neighbors (KNN) to leverage the strengths of both algorithms.
2. The algorithm first uses KNN to classify an MRI image based on its similarity to labeled training images. If KNN is uncertain or confused, it then uses SVM to classify the image by finding the optimal separating hyperplane between tumor and non-tumor classes.
3. The authors implemented this hybrid SVM-KNN algorithm in MATLAB and were able to successfully classify test MRI images as depicting a normal brain or an abnormal brain with a tumor.
I0341042048
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.
A Framework of Secured and Bio-Inspired Image Steganography Using Chaotic Enc...
This document proposes a novel secured image steganography technique called CEGAO that uses chaotic encryption and genetic algorithm optimization. It encrypts a secret image using a logistic map before embedding it in the least significant bits of cover images. To extract the secret image, the recipient decrypts and retrieves the secret image blocks from the stego image using the same logistic map. The technique achieves PSNR values up to 48.55 dB and SSIM values over 0.99, outperforming conventional LSB techniques. While performance is encouraging, the authors aim to further improve resistance against statistical attacks and optimize other aspects of the algorithm.
Pixel Recursive Super Resolution. Google Brain
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
Fcv poster efros
The document proposes an object detection method that combines the effectiveness of discriminative detectors with the explicit correspondence of nearest-neighbor approaches. It trains a separate linear SVM classifier for each exemplar in the training set, where each detector is defined by a single positive instance and millions of negatives. While each detector is specific to its exemplar, the paper finds that an ensemble of these Exemplar-SVMs offers good generalization. Their method achieves performance on par with more complex latent part-based models, with only a modest increase in computational cost.
Target Detection by Fuzzy Gustafson-Kessel Algorithm
Many commercially available radar systems offer a range of filter options but the problem of clutter rejection for target detection is still present in a number of situations. Rejection of clutter and detection of targets from radar captured data is a challenging task. Raw data captured by radar are not always scaled. A normalization technique has been proposed which transforms the radar captured data into 8 bit. As 8 bit data is easy to analyze and visualize. A modification on Fuzzy c-means has been done by developing Fuzzy Gustafson–Kessel (FGK) algorithm and the result shows robustness of this proposed method.
M017427985
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.
proposal_pura
This document proposes a linear recurrent convolutional neural network model for segment-based multiple object tracking in video. The model takes images as input and uses a CNN to classify superpixels, then performs segmentation and uses nonlinear NNs and a linear recurrent tracker layer to match segments over time. The objectives are to improve the tracker layer efficiency by modifying the matrix inverse and determine parameters for the model. Evaluation will use a dataset with ground truth segmentation and optical flow to train and compare to state-of-the-art methods.
Matrioska tracking keypoints in real-time
Title PDF: MATRIOSKA: A Multi-level Approach to Fast Tracking by Learning | ICIAP 2013
In this paper we propose a novel framework for the detection and tracking in real-time of unknown object in a video stream. We decompose the problem into two separate modules: detection and learning. The detection module can use multiple keypoint-based methods (ORB, FREAK, BRISK, SIFT, SURF and more) inside a fallback model, to correctly localize the object frame by frame exploiting the strengths of each method. The learning module updates the object model, with a growing and pruning approach, to account for changes in its appearance and extracts negative samples to further improve the detector performance. To show the effectiveness of the proposed tracking-by-detection algorithm, we present numerous quantitative results on a number of challenging sequences in which the target object goes through changes of pose, scale and illumination.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
An improved particle filter tracking
This document describes an improved particle filter tracking system that uses both color and moving edge information. It aims to address limitations of existing color-based particle filter tracking systems, such as inaccurate tracking when the target and background have similar colors, occlusion occurs, or the target is deformed. The proposed system selects an appropriate bounding box around the target using moving edge information to maintain an accurate target model during tracking. An experiment using 100 targets in 10 video clips showed the new system achieved a 94.6% accuracy rate for tracking, higher than an existing color-based particle filter system. It also had a 91.8% accuracy for occluded targets, much better than the previous system.
The super resolution technology 2016
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
2019 cvpr paper_overview
Statistics and Visualization of acceptance rate, main keyword for the main Computer Vision conference (CVPR)
20 CVPR Paper Review
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Distance-based bias in model-directed optimization of additively decomposable...
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Arp zmp
This document proposes an adaptive rood pattern search (ARPS) algorithm for fast block matching motion estimation. ARPS uses adjustable search patterns centered around a predicted motion vector value to quickly locate the minimum matching error point. It employs a checking bit-map and zero-motion prejudgment to avoid duplicate computations and benefit sequences with small motion. Experimental results show ARPS achieves 94-447x speedup over full search with less than 0.12dB drop in PSNR quality compared to full search for most sequences.
A Deep Belief Network Approach to Learning Depth from Optical Flow
The document describes using a deep belief network to learn depth from optical flow in videos. A biologically inspired model is used to generate motion features from video frames. These motion features are then used as input to a deep neural network that is trained to predict depth maps. The network is initialized using unsupervised pre-training of restricted Boltzmann machines and then fine-tuned with supervised backpropagation. Computer-generated graphics are used to obtain labeled training data of video frames paired with ground truth depth maps. The results show improved depth prediction over standard classifiers, demonstrating the potential of unsupervised feature learning for computer vision tasks.
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Anal...
Paper: https://arxiv.org/abs/2004.00448
Github: https://github.com/clovaai/cutblur
Youtube: https://youtu.be/MQR5mnsCvbM
IEEE 2014 Matlab Projects
List of IEEE Matlab Projects. It Contains the IEEE Projects in the Language Matlab with ieee publication year 2014
What's hot (20)
A Framework of Secured and Bio-Inspired Image Steganography Using Chaotic Enc...
A Framework of Secured and Bio-Inspired Image Steganography Using Chaotic Enc...
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...
Target Detection by Fuzzy Gustafson-Kessel Algorithm
Target Detection by Fuzzy Gustafson-Kessel Algorithm
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...
Distance-based bias in model-directed optimization of additively decomposable...
Distance-based bias in model-directed optimization of additively decomposable...
A Deep Belief Network Approach to Learning Depth from Optical Flow
A Deep Belief Network Approach to Learning Depth from Optical Flow
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Anal...
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Anal...
Similar to Poster: AVSS 2012
Poster: ICPR 2008
This paper presents a technique to improve Gaussian mixture models for robust object detection by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more background models. The proposed method eliminates drawbacks of poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and instability with varying operating environments. Quantitative and qualitative evaluations on test video sequences show the proposed technique achieves lower error rates and better visual results compared to existing methods.
Hyperspectral unmixing using novel conversion model.ppt
The document presents a novel hyperspectral unmixing approach called uccm-SVM that converts the abundance quantification problem into a classification problem using support vector machines. The approach is tested on both simulated and real hyperspectral images and is shown to outperform traditional mean-based techniques like FCLS in terms of accuracy while having lower computational costs for smaller training set sizes. Future work to improve the method includes enhancing performance while reducing computation for larger training sets.
Poster: MMSP 2008
This document presents a hybrid object detection technique that combines adaptive background modeling using Gaussian mixture models with basic background subtraction. It constructs a pixel model for each pixel using a mixture of Gaussian distributions to model multi-modal distributions from moving foregrounds and repetitive background motions. The technique reduces noise, shadows, and trailing effects while maintaining stability across varying environments. Evaluation on 14 test sequences shows the technique achieves lower error rates than other methods with less variation across learning rates.
Poster: Monash Research Month 2008
This document presents an intelligent surveillance framework for real-time abnormal behavior recognition from video streams. The framework uses background modeling and Gaussian mixture models to detect foreground regions and track object movements. It then analyzes the detected behaviors to identify any unusual or malicious group activities. The goal is to help human monitors more reliably identify security threats by automating some detection tasks.
HiPEAC2022_António Casimiro presentation
ML Robustness in VEDLIoT. Antonio Casimiro. Workshop on Deep Learning for IoT (DL4IoT), co-located with HiPEAC 2022, Budapest, Hungary, June 2022
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
The document evaluates the performance of various foreground extraction algorithms for object detection in visual surveillance. It analyzes three background modeling techniques (change detection mask, median, histogram) and two background subtraction algorithms (frame difference, approximate median). Experimental results on test videos show that background modeling using the median value technique and background subtraction using frame differencing provides the most robust and efficient combination. Processing times are reported for different combinations of algorithms. The study concludes that the median-based approach has good computational efficiency and robustness for background modeling.
Effective Object Detection and Background Subtraction by using M.O.I
This paper proposes efficient motion detection and people counting based on background subtraction using dynamic threshold approach with mathematical morphology. Here these different methods are used effectively for object detection and compare these performance based on accurate detection. Here the techniques frame differences, dynamic threshold based detection will be used. After the object foreground detection, the parameters like speed, velocity motion will be determined. For this, most of previous methods depend on the assumption that the background is static over short time periods. In dynamic threshold based object detection, morphological process and filtering also used effectively for unwanted pixel removal from the background. The background frame will be updated by comparing the current frame intensities with reference frame. Along with this dynamic threshold, mathematical morphology also used which has an ability of greatly attenuating color variations generated by background motions while still highlighting moving objects. Finally the simulated results will be shown that used approximate median with mathematical morphology approach is effective rather than prior background subtraction methods in dynamic texture scenes and performance parameters of moving object such sensitivity, speed and velocity will be evaluated by using MOI.
Dj31514517
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
Dj31514517
This document summarizes a research paper on background subtraction techniques for motion detection in video. It describes a proposed technique that stores and compares past pixel values to the current value to determine if a pixel belongs to the background or foreground. It also discusses using a k-means algorithm and Gaussian mixture model to build a probabilistic background model and classify pixels. The paper evaluates different shadow detection approaches and finds RGB color spaces perform best for segmentation and shadow removal.
Multiple Person Tracking with Shadow Removal Using Adaptive Gaussian Mixture ...
Person detection in a video surveillance system is major concern in real world. Several application likes abnormal event detection, congestion analysis, human gait characterization, fall detection, person identification, gender classification and for elderly people. In this algorithm, we use GMM method in background subtraction for multi person detection because of Gaussian Mixture Model (GMM) model is one such popular method this give a real time object detection. There is still not robustly but Multi person tracking with shadow removal fill this gap, in this work, HOG-LBP hybrid approach with GMM algorithm is presented for Multi person tracking with Shadow removal.
Image Restoration for 3D Computer Vision
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
105번째 논문리뷰,
오늘 소개 드릴 논문은 2020 CVPR에서 발표된 Meta-Transfer Learning for Zero-Shot Super-Resolution 라는 논문입니다!
제목에서 유추가 가능하신것 처럼 학습 데이터없이 저해상도 사진을 고해상도 사진으로 바꿔주는 Zero Shot Super Resolution을 위한 Meta Transfer Learning을 소개합니다. Internal Learning에 적합한 General한 초기 parameter를 찾는것에 기반하여 한번의 Gradient Update만으로 최적의 성능을 보여주는것 방법에 대해서 소개합니다.
논문에 대한 자세한 리뷰를 이미지 처리팀 김선옥 님이 자세한 리뷰 도와주셨습니다!
https://youtu.be/lEqbXLrUlW4
Poster: ICME 2010
This document presents an approach for panic-driven event detection from surveillance video streams without using track and motion features. It extracts frame-level and temporal features from foreground blobs detected through background subtraction. These features are used to train support vector machine models for different events. The approach is evaluated on unseen scenarios from a public dataset and shows promising detection results without additional training, outperforming an approach using only optical flow. Experimental results demonstrate the detection of events like meetings, runaways, and fights in different environments.
2009 spie hmm
This document discusses using hidden Markov models (HMMs) for unsupervised learning in hyperspectral image classification. It proposes an HMM-based probability density function classifier that models hyperspectral data using a reduced feature space. The approach uses an unsupervised learning scheme for maximum likelihood parameter estimation, combining both model selection and estimation. This HMM method can accurately model and synthesize approximate observations of true hyperspectral data in a reduced feature space without relying on supervised learning.
Vensoft IEEE 2014 2015 Matlab Projects tiltle Image Processing Wireless Signa...
Vensoft IEEE 2014 2015 Matlab Projects tiltle Image Processing Wireless Signa...Vensoft Technologies
This document describes six MATLAB projects available from VenSoft Technologies for the 2014-2015 academic year. It provides the titles, abstracts, publishing details, and index terms for each project. The projects involve topics such as sparse unmixing of hyperspectral data, mixed noise removal, subspace matching pursuit, exploiting spectral a priori information, gradient histogram preservation for image denoising, and image set-based collaborative representation for face recognition.Proximity Detection in Distributed Simulation of Wireless Mobile Systems
The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.
Kurmi 2015-ijca-905317
The document describes an image fusion approach that uses adaptive fuzzy logic modeling for global processing followed by Markov random field modeling for local processing.
It begins by introducing image fusion and its applications. It then discusses existing fusion approaches and their limitations. The proposed approach first uses an adaptive fuzzy logic model to minimize redundant information globally. It then applies Markov random field modeling locally for fusion. Experimental results showed the proposed approach improved the universal image quality index by 30-35% compared to fusion with Markov random field modeling alone.
MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...
Magnetic Resonance (MR) image is a medical image technique required enormous data to be stored and
transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the
performance of the compression scheme. In this paper we extended the commonly used algorithms to image
compression and compared its performance. For an image compression technique, we have linked different
wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau
wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7) wavelet transform with Set Partition in
Hierarchical Trees (SPIHT) algorithm. A novel image quality index with highlighting shape of histogram
of the image targeted is introduced to assess image compression quality. The index will be used in place of
existing traditional Universal Image Quality Index (UIQI) “in one go”. It offers extra information about
the distortion between an original image and a compressed image in comparisons with UIQI. The proposed
index is designed based on modelling image compression as combinations of four major factors: loss of
correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate
and applicable in various image processing applications. One of our contributions is to demonstrate the
choice of mother wavelet is very important for achieving superior wavelet compression performances based
on proposed image quality indexes. Experimental results show that the proposed image quality index plays
a significantly role in the quality evaluation of image compression on the open sources “BrainWeb:
Simulated Brain Database (SBD) ”.
M.E Computer Science Remote Sensing Projects
List of Remote Sensing IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Remote Sensing for M.E Computer Science students.
M.Phil Computer Science Remote Sensing Projects
List of Remote Sensing IEEE 2006 Projects. It Contains the IEEE Projects in the Domain Remote Sensing for M.Phil Computer Science students.
Similar to Poster: AVSS 2012 (20)
Hyperspectral unmixing using novel conversion model.ppt
Hyperspectral unmixing using novel conversion model.ppt
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
Effective Object Detection and Background Subtraction by using M.O.I
Effective Object Detection and Background Subtraction by using M.O.I
Multiple Person Tracking with Shadow Removal Using Adaptive Gaussian Mixture ...
Multiple Person Tracking with Shadow Removal Using Adaptive Gaussian Mixture ...
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
[딥논읽] Meta-Transfer Learning for Zero-Shot Super-Resolution paper review
Vensoft IEEE 2014 2015 Matlab Projects tiltle Image Processing Wireless Signa...
Vensoft IEEE 2014 2015 Matlab Projects tiltle Image Processing Wireless Signa...
Proximity Detection in Distributed Simulation of Wireless Mobile Systems
Proximity Detection in Distributed Simulation of Wireless Mobile Systems
MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...
MR Image Compression Based on Selection of Mother Wavelet and Lifting Based W...
More from Mahfuzul Haque
Dependency inversion using ports and adapters
This document discusses the Ports and Adapters architecture for decoupling an application's core business logic from external dependencies and allowing different services to be plugged in. The aims are to decouple the core logic, allow different services to be plugged in and removed easily, and make the application framework agnostic. An example order processing application is used to show how it evolves from being tightly coupled to external dependencies to following the Ports and Adapters pattern using interfaces, ports, and adapters to isolate the core logic. Code examples are provided in a GitHub repository linked in the document.
Resilient machine learning systems for health analytics
Tech companies are shifting more towards machine learning or AI-first strategy. What does that mean to them? What does that mean to us? Growing number of aging population and shrinking funding are putting enormous pressure to the overall healthcare system for keeping up with the desired quality of care with limited resources. Thus, there is an increasing focus on machine learning capabilities for just-in-time alerts to predict various future events so that undesirable incidents can be reduced by giving attention to the right people at the right time. This talk explores the underlying framework behind such capabilities, various strategies for a resilient system, and the role of a machine learning platform.
Talk 2012-icmew-event
This document proposes an approach for abnormal event detection in unseen scenarios using blob-level statistical analysis of video frames. The approach extracts frame-level features from foreground blobs using measures like blob area, count, and distance. These are transformed into temporal features considering speed and order. Event models are built offline using these features and an SVM classifier. During operation, only selected frame and temporal features are extracted in real-time, without additional training or tuning. Experiments on public safety and crowd datasets show the approach can detect events in unseen environments, outperforming optical flow-based methods.
Talk 2011-buet-perception-event
This document discusses a new technique called Perceptual Mixture of Gaussians (PMOG) for background subtraction in video analytics. PMOG incorporates characteristics of human visual perception to improve detection quality. It uses a realistic background value prediction based on the most recent observation. Detection threshold is set based on Weber's law and a just-noticeable difference threshold. Frame-level features are extracted from foreground blobs and transformed into temporal features for event detection. Experiments show PMOG has higher stability across environments and superior detection quality compared to other techniques. It provides an effective approach for abnormal event detection without requiring context information.
Talk 2010-monash-seminar-panic-driven-event-detection
This document presents a method for panic-driven event detection from surveillance video without using track and motion features. The method extracts frame-level features from each video frame independently, then transforms those into temporal features considering speed and order to characterize events. It was tested on four events from the CAVIAR dataset with over 80% accuracy and shown to detect abnormal events in three sites from the UMN dataset. The approach aims to develop a more generic event detection framework compared to tracking or motion-based methods.
Talk 2009-monash-seminar-perception
This document proposes a new object detection technique based on human visual perception. The technique uses a pixel-based mixture of Gaussians (MOG) model for environment modelling. It incorporates two human perceptual characteristics: a model reference point instead of the Gaussian mean, and a model extent based on a human perceivable intensity threshold rather than the Gaussian variance. Experiments on 50 test sequences show the technique achieves higher stability across learning rates and better detection quality at fast learning rates compared to existing techniques. Future work will explore applying this approach to multimodal scenarios.
Talk 2009-monash-seminar-intelligent-video-surveillance
This document describes a behaviour recognition framework for intelligent visual surveillance. It discusses the need for intelligent surveillance due to the large number of cameras now deployed. The framework aims to aid human monitors by automatically detecting abnormal group behaviors using computer vision and machine learning techniques. It extracts both frame-level and temporal features from video and classifies behaviors like mob violence, crowding, and panic behaviors using individual SVM classifiers. The framework includes components for background modeling, feature extraction at both frame and temporal levels, and behavior classification. It was implemented in a system called VSTK and evaluated on benchmark datasets.
Talk 2009-monash-open-day-surveillance
The document discusses the increasing use of video surveillance cameras for security purposes. While cameras are common in many public and private locations, it is impossible for human operators to constantly monitor the many video streams from thousands of cameras. Therefore, new technologies use computer vision and machine learning techniques to automatically detect events and alert security. Researchers are developing new surveillance applications that can analyze video to separate moving objects, detect undesirable events, and notify security. These technologies aim to provide security monitoring with minimum human involvement.
Talk 2007-monash-seminar-behavior-recognition-framework
The document proposes a real-time behavior recognition framework for visual surveillance. It aims to recognize unusual, unsafe, and abnormal human and group behaviors from surveillance video in real-time. This would help human monitors and improve surveillance system reliability. The framework includes environment modeling to identify active regions, feature extraction and agent classification, agent tracking handling occlusion, and behavior recognition by learning and detecting patterns. Literature on these components is also reviewed, including using Gaussian mixture models for environment modeling and pixel-based approaches.
Kb hmm
Hidden Markov models (HMMs) are statistical models used to predict hidden or latent states from observable states. HMMs assume the system being modeled is a Markov process with unknown parameters. The challenge is to determine the hidden parameters from observable parameters without directly observing the hidden states. HMMs have transition probabilities between hidden states and emission probabilities of observable states from hidden states. Algorithms like Baum-Welch, Viterbi, and forward-backward are used to estimate HMM parameters from data and predict hidden states.
Kb gait-recognition
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
Kb behaviour-recognition
The document discusses different approaches to abnormal behavior recognition: supervised, unsupervised, and semi-supervised. The supervised approach relies on clearly labeled normal and abnormal data but abnormal behavior is rare and undefined. Unsupervised approaches either cluster patterns or build a database of normal behavior patterns to detect abnormalities. Semi-supervised uses labeled normal data to build a normal model and then learns the abnormal model unsupervised. All approaches still have issues like insufficient labeled data or inconsistent manual labeling.
Talk 2012-icmew-perception
The document proposes a perceptual mixture-of-Gaussians approach to background subtraction that adapts dynamically based on principles of human visual perception. It models the relationship between detectable foreground pixels and background pixels based on Weber's law of just-noticeable differences. This results in piecewise linear relationships and different adaptation speeds for scotopic and photopic vision. An evaluation of 50 test sequences shows the proposed approach achieves more realistic background value prediction, higher stability across scenarios, and superior detection quality compared to other statistical methods.
Poster: Monash Research Month 2009
The document describes an intelligent security application being developed to detect unusual events from surveillance video streams. It uses computer vision and artificial intelligence techniques like foreground region detection, feature extraction and temporal feature analysis to help human monitors take early action against malicious events. The application separates foreground regions using adaptive background modeling, extracts scene-level features from those regions, and analyzes temporal variations in the features to map them to high-level event models.
Poster: Monash Research Month 2007
This document proposes a robust moving object detection method for visual surveillance. It uses a Gaussian mixture model (GMM) that models each pixel as a mixture of Gaussian distributions to address multi-modal distributions from illumination variations or repetitive background motions. The technique analyzes input video frames to build GMM pixel models over time. Experimental results on test sequences show the proposed method has fewer false classifications than existing GMM methods, indicating it performs better moving object detection.
Poster: EII Workshop 2007
Gait recognition from video sequences has potential for human identification but faces challenges. Gait is the way a person walks and can be captured from video without contact. However, factors like viewing angle, lighting, and obscured body parts impact accuracy. Researchers are working to develop robust algorithms that can recognize gait across varying conditions and datasets. While progress has been made, gait recognition accuracy is still not sufficient for real-world applications due to challenges that must be addressed through further research.
More from Mahfuzul Haque (16)
Resilient machine learning systems for health analytics
Resilient machine learning systems for health analytics
Talk 2010-monash-seminar-panic-driven-event-detection
Talk 2010-monash-seminar-panic-driven-event-detection
Talk 2009-monash-seminar-intelligent-video-surveillance
Talk 2009-monash-seminar-intelligent-video-surveillance
Talk 2007-monash-seminar-behavior-recognition-framework
Talk 2007-monash-seminar-behavior-recognition-framework
Recently uploaded
CAKE: Sharing Slices of Confidential Data on Blockchain
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Things to Consider When Choosing a Website Developer for your Website | FODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
In this blog post, we'll delve into the intersection of AI and app development in Saudi Arabia, focusing on the food delivery sector. We'll explore how AI is revolutionizing the way Saudi consumers order food, how restaurants manage their operations, and how delivery partners navigate the bustling streets of cities like Riyadh, Jeddah, and Dammam. Through real-world case studies, we'll showcase how leading Saudi food delivery apps are leveraging AI to redefine convenience, personalization, and efficiency.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Recently uploaded (20)
CAKE: Sharing Slices of Confidential Data on Blockchain
CAKE: Sharing Slices of Confidential Data on Blockchain
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Things to Consider When Choosing a Website Developer for your Website | FODUU
Things to Consider When Choosing a Website Developer for your Website | FODUU
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
AI-Powered Food Delivery Transforming App Development in Saudi Arabia.pdf
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Poster: AVSS 2012
- 1. Background Subtraction for Real-time Video Analytics Based on Multi-hypothesis Mixture-of-Gaussians Mahfuzul Haque and Manzur Murshed Gippsland School of Information Technology, Monash University, Victoria 3842, Australia Email: {Mahfuzul.Haque, Manzur.Murshed}@monash.edu 1 Abstract Robust background subtraction (BS) is essential for high quality foreground detection in most video analytics systems. Recent BS techniques achieve superior detection quality mostly by exploiting the complementary strengths of multiple background models. Consequently, these techniques fail to meet the operational requirements of real-time video analytics. The proposed BS technique, named multi-hypothesis mixture-of-Gaussians (MH-MOG), maintains a single background model based on perception-aware mixture-of-Gaussians and then, generates multiple detection hypotheses with different processing bases. Finally, only during the detection stage, the complementary strengths of the hypotheses are exploited to achieve superior detection quality without significant computational overhead. 2 Dynamic Background Subtraction 3 Multiple Detection Hypotheses for Superior Detection Quality Detection Decision Detection Decision Background Model Video Frame Hypothesis1 Hypothesis2 … Hypothesisn Model1 Model2 … Modeln Foreground Mask Hypothesis1 Hypothesis2 Hypothesisn Model Proposed Conventional Dynamic background subtraction is an essential precursor in most video analytics systems for moving foreground detection. The quality of foreground detection directly impacts the performance of subsequent processing tasks. … To achieve superior detection quality conventional approaches use the complementary strengths of multiple detection hypotheses that are originated from different background models while the proposed technique uses a single underlying background model to generate complementary detection hypotheses. 4 The Proposed Background Subtraction Technique (MH-MOG) MH-MOG Background Model Perception Inspired Detection Hypothesis Confidence Level for Detection Hypothesis Detection Algorithm Probabilistic Detection Hypothesis Confidence Level for Detection Hypothesis High Quality Foreground Mask Incoming Video Stream In the proposed background subtraction technique, a single background model is maintained based on observed video frames. Then based on this background model two independent detection hypotheses (e.g., perception inspired and probabilistic) are generated. For both hypotheses, associated confidence levels are computed based on spatial detection results in the corresponding hypothesis space. Finally, all these information is used by the detection algorithm to produce high quality foreground mask by maximising the complementary strengths of both hypotheses [1]. 6 Perception inspired detection hypothesis 5 Background Modelling Observed intensity value: m Mean: µ Standard deviation: σ Weight: ω P(x) Intensity The background of the operating environment is modelled at pixel-level by maintaining at most N observed intensity values (m1, m2, …, mN). For each sample, associated Gaussian variables (µ, σ, and ω) are maintained to determine the order of the samples based on observation frequency. 8 Experiments 0 m1 m2 Intensity m3 255 A confidence interval is determined for each believed-to-bebackground intensity value based on the characteristics of human visual system in perceiving noticeable intensity deviation from background (Weber’s Law). Observed intensity values are classified as background based on their membership in any background confidence interval [2]. 9 Quantitative Evaluation Quantitative comparison: This figure shows overall (ALL), dataset-wise (PETS, WF, UCF, IBM, CAV, VSSN), and sequenceclass-wise (SR, MM, LC) performance comparisons. Unlike perception inspired hypothesis, no subset of samples is chosen as background for intensity comparison. Rather a probabilistic formulation involving all Gaussian components is used [3] for background/foreground classification. This hypothesis shows higher foreground sensitivity and thus recovers missing foreground regions due to intensity thresholding by the perception inspired hypothesis. 10 Visual Comparison First Frame More than 50 test sequences were selected from eight different datasets including PETS, Wallflower, IBM, VSSN06, CAVIAR, and UCF and categorised in following classes based on the characteristics of the operating environments: low contrast foreground (LC), shadows and reflections (SR), multi-modal background (MM), indoor (INDOOR), and outdoor (OUTDOOR). 7 Probabilistic detection hypothesis Test Frame Ground Truth MOG (S&G) MOG (Lee) ViBe MH-MOG MOG (S&G) – TPAMI 2000, MOG (Lee) – TPAMI 2005, ViBe – TIP 2011, and MH-MOG – Proposed. [1] M. Haque and M. Murshed, Background Subtraction for Real-time Video Analytics Based on Multi-hypothesis Mixture-of-Gaussians, IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Beijing, China, 2012. [2] M. Haque and M. Murshed, Robust Background Subtraction Based on Perceptual Mixture-of-Gaussians with Dynamic Adaptation Speed, IEEE International Workshop on Advances in Automated Multimedia Surveillance for Public Safety, Melbourne, Australia, 2012. [3] D.-S. Lee. Effective Gaussian mixture learning for video background subtraction, IEEE TPAMI, 27(5):827– 832, 2005. The images shown in the header has been taken from http://www.informationliberation.com