Despite recent advances in joint processing of images,
sometimes it may not be as effective as single image
processing for object discovery problems. In this paper while
aiming for common object detection, we attempt to address
this problem by proposing a novel QCCE: Quality Constrained
Co-saliency Estimation method. The approach here is to iteratively
update the saliency maps through co-saliency estimation
depending upon quality scores, which indicate the degree of
separation of foreground and background likelihoods (the easier
the separation, the higher the quality of saliency map). In this
way, joint processing is automatically constrained by the quality
of saliency maps. Moreover, the proposed method can be applied
to both unsupervised and supervised scenarios, unlike other
methods which are particularly designed for one scenario only.
Experimental results demonstrate superior performance of the
proposed method compared to the state-of-the-art methods.
VARIATION-FREE WATERMARKING TECHNIQUE BASED ON SCALE RELATIONSHIPcsandit
Most watermark methods use pixel values or coefficients as the judgment condition to embed or
extract a watermark image. The variation of these values may lead to the inaccurate condition
such that an incorrect judgment has been laid out. To avoid this problem, we design a stable
judgment mechanism, in which the outcome will not be seriously influenced by the variation.
The principle of judgment depends on the scale relationship of two pixels. From the observation
of common signal processing operations, we can find that the pixel value of processed image
usually keeps stable unless an image has been manipulated by cropping attack or halftone
transformation. This can greatly help reduce the modification strength from image processing
operations. Experiment results show that the proposed method can resist various attacks and
keep the image quality friendly.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
This document presents a new approach for multiclass image segmentation and categorization using Bayesian networks and spatial Markov kernels. It first constructs an over-segmented image and Bayesian network to model relationships between image elements. Interactive segmentation is performed to match pixels to an outline provided by the user. The segmented image is then categorized using a spatial Markov kernel algorithm based on visual keywords assigned to image blocks. The approach achieves 93.5% accuracy on test images. It provides a probabilistic way to model image segmentation and allows new knowledge to be incorporated through the Bayesian network framework.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
Most of the existing co-segmentation methods are usually
complex, and require pre-grouping of images, fine-tuning a
few parameters and initial segmentation masks etc. These
limitations become serious concerns for their application on
large scale datasets. In this paper, Group Saliency Propagation
(GSP) model is proposed where a single group saliency
map is developed, which can be propagated to segment the
entire group. In addition, it is also shown how a pool of these
group saliency maps can help in quickly segmenting new
input images. Experiments demonstrate that the proposed
method can achieve competitive performance on several
benchmark co-segmentation datasets including ImageNet,
with the added advantage of speed up.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of MapsNAVER Engineering
Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information. This task is inherently challenging since many photos have only few, possibly ambiguous cues to their geolocation. Recent work has cast this task as a classification problem by partitioning the earth into a set of discrete cells that correspond to geographic regions. The granularity of this partitioning presents a critical trade-off; using fewer but larger cells results in lower location accuracy while using more but smaller cells reduces the number of training examples per class and increases model size, making the model prone to overfitting. To tackle this issue, we propose a simple but effective algorithm, combinatorial partitioning, which generates a large number of fine-grained output classes by intersecting multiple coarse-grained partitionings of the earth. Each classifier votes for the fine-grained classes that overlap with their respective coarse-grained ones. This technique allows us to predict locations at a fine scale while maintaining sufficient training examples per class. Our algorithm achieves the state-of-the-art performance in location recognition on multiple benchmark datasets.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
VARIATION-FREE WATERMARKING TECHNIQUE BASED ON SCALE RELATIONSHIPcsandit
Most watermark methods use pixel values or coefficients as the judgment condition to embed or
extract a watermark image. The variation of these values may lead to the inaccurate condition
such that an incorrect judgment has been laid out. To avoid this problem, we design a stable
judgment mechanism, in which the outcome will not be seriously influenced by the variation.
The principle of judgment depends on the scale relationship of two pixels. From the observation
of common signal processing operations, we can find that the pixel value of processed image
usually keeps stable unless an image has been manipulated by cropping attack or halftone
transformation. This can greatly help reduce the modification strength from image processing
operations. Experiment results show that the proposed method can resist various attacks and
keep the image quality friendly.
Object Shape Representation by Kernel Density Feature Points Estimator cscpconf
This paper introduces an object shape representation using Kernel Density Feature Points
Estimator (KDFPE). In this method we obtain the density of feature points within defined rings
around the centroid of the image. The Kernel Density Feature Points Estimator is then applied to
the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of
image representation shows improved retrieval rate when compared to Density Histogram
Feature Points (DHFP) method. Analytic analysis is done to justify our method and we compared our results with object shape representation by the Density Histogram of Feature Points (DHFP) to prove its robustness.
This document presents a new approach for multiclass image segmentation and categorization using Bayesian networks and spatial Markov kernels. It first constructs an over-segmented image and Bayesian network to model relationships between image elements. Interactive segmentation is performed to match pixels to an outline provided by the user. The segmented image is then categorized using a spatial Markov kernel algorithm based on visual keywords assigned to image blocks. The approach achieves 93.5% accuracy on test images. It provides a probabilistic way to model image segmentation and allows new knowledge to be incorporated through the Bayesian network framework.
[PDF] Automatic Image Co-segmentation Using Geometric Mean Saliency (Top 10% ...Koteswar Rao Jerripothula
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
Most of the existing co-segmentation methods are usually
complex, and require pre-grouping of images, fine-tuning a
few parameters and initial segmentation masks etc. These
limitations become serious concerns for their application on
large scale datasets. In this paper, Group Saliency Propagation
(GSP) model is proposed where a single group saliency
map is developed, which can be propagated to segment the
entire group. In addition, it is also shown how a pool of these
group saliency maps can help in quickly segmenting new
input images. Experiments demonstrate that the proposed
method can achieve competitive performance on several
benchmark co-segmentation datasets including ImageNet,
with the added advantage of speed up.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of MapsNAVER Engineering
Image geolocalization is the task of identifying the location depicted in a photo based only on its visual information. This task is inherently challenging since many photos have only few, possibly ambiguous cues to their geolocation. Recent work has cast this task as a classification problem by partitioning the earth into a set of discrete cells that correspond to geographic regions. The granularity of this partitioning presents a critical trade-off; using fewer but larger cells results in lower location accuracy while using more but smaller cells reduces the number of training examples per class and increases model size, making the model prone to overfitting. To tackle this issue, we propose a simple but effective algorithm, combinatorial partitioning, which generates a large number of fine-grained output classes by intersecting multiple coarse-grained partitionings of the earth. Each classifier votes for the fine-grained classes that overlap with their respective coarse-grained ones. This technique allows us to predict locations at a fine scale while maintaining sufficient training examples per class. Our algorithm achieves the state-of-the-art performance in location recognition on multiple benchmark datasets.
FORGERY (COPY-MOVE) DETECTION IN DIGITAL IMAGES USING BLOCK METHODeditorijcres
AKHILESH KUMAR YADAV, DEENBANDHU SINGH, VIVEK KUMAR
Department of Computer Science and Engineering
Babu Banarasi Das University, Lucknow
akhi2232232@gmail.com, deenbandhusingh85@gmail.com, vivek.kumar0091@gmail.com
ABSTRACT- Digital images can be easily modified using powerful image editing software. Determining whether a manipulation is innocent of sharpening from those which are malicious, such as removing or adding parts to an image is the topic of this paper. In this paper we focus on detection of a special type of forgery-the Copy-Move forgery, in this part of the original image is copied moved to desired location in the same image and pasted. The proposed method compress images using DWT (discrete wavelet transform) and divided into blocks and choose blocks than perform feature vector calculation and lexicographical sorting and duplicated blocks are identified after sorting. This method is good at some manipulation/attack likes scaling, rotation, Gaussian noise, smoothing, JPEG compression etc.
INDEX TERMS- Copy-Move forgery, Wavelet Transform, Lexicographical Sorting, Region Duplication Detection.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
The document compares Gaussian filtering and bilateral filtering on RGB images. Gaussian filtering replaces each pixel with a weighted average of neighboring pixels, with weights determined by a Gaussian function of distance. This blurs the image while reducing high-frequency components. Bilateral filtering also considers pixel intensity differences, using a range Gaussian to give higher weight to similar pixels, thus preserving edges while smoothing. The effect of varying the spatial and range parameters is examined, showing bilateral filtering better retains edges through the additional range parameter.
This document describes an implementation of artistic style learning using a neural network approach. The algorithm is based on the VGG convolutional neural network and uses the NVidia CUDA platform to speed up computations. The implementation reconstructs content using loss functions on convolutional layer outputs and represents style using Gram matrices of filter correlations. A new image is generated by minimizing a combined loss of content and style. Experiments show improved results over the original algorithm by using softplus activations and average pooling.
The document provides an introduction to the Finite Element Method (FEM). It discusses that FEM is a numerical technique used to find approximate solutions to partial differential equations. It involves discretizing a continuous domain into smaller, separate elements. Properties are assumed to be constant within each element. Elements are connected at nodes and assembled to model the entire structure. The document outlines the history and development of FEM. It also discusses different numerical methods used in engineering analysis and highlights the advantages, disadvantages, and limitations of using FEM.
This document describes a project that uses photometric stereo to reconstruct 3D surfaces from images taken under different lighting conditions on a computer screen. Photometric stereo uses variations in pixel intensities across images to estimate surface normals and reconstruct the 3D shape. The project creates a MATLAB program that performs photometric stereo in real-time by flashing different light patterns on a screen and capturing images with a webcam. By using singular value decomposition, the program can reconstruct surfaces without knowing the exact positions of the light sources, overcoming a limitation of traditional photometric stereo. The reconstruction contains noise but demonstrates photometric stereo in a less controlled environment. Further work could explore tradeoffs of the method and improve efficiency.
Seed net automatic seed generation with deep reinforcement learning for robus...NAVER Engineering
본 논문에서는 interactive segmentation 문제를 풀기 위하여 deep reinforcement learning을 활용한 seed gereration 기법을 제안한다. Interactive segmentation 문제의 이슈 중 하나는 사용자의 개입을 최소화하는 것이다. 본 논문에서 제안하는 시스템이 사용자를 대신하여 인공적인 seed를 생성하게 된다. 사용자는 initial seed 정보만을 제공하면 된다. 우리는 optimal seed point 정의의 모호함으로 인해 supervised 기법을 사용하여 학습하기 어려운 점을 reinforcement learning 기법을 사용하여 극복하였다. Seed generation 문제에 맞도록 MDP를 정의하여 deep-q-network를 성공적으로 학습하였다. 우리는 MSRA10K 데이터셋에 대하여 학습을 진행하여 기존 segmentation 알고리즘의 부정확한 initial 결과 대비 우수한 성능을 보였다.
NEIGHBOUR LOCAL VARIABILITY FOR MULTIFOCUS IMAGES FUSIONsipij
This document presents a new method for fusing multi-focus images called the Neighbor Local Variability (NLV) method. The NLV method calculates the local variability of each pixel based on the quadratic difference between the pixel value and those of neighboring pixels. This variability is used to weight pixel values when fusing the images. The optimal size of the neighborhood considered depends on the variance and size of the blur filter, and can be estimated using a provided model. The NLV method is compared to other fusion methods and is shown to produce better results based on the Root Mean Square Error evaluation metric.
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Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Imagesijtsrd
A new artifact removal method as cascade of Gaussian fuzzy edge decider and fuzzy image correction is proposed. In this design, a highly compressed i.e. low bit rate image is considered. Here, each overlapped block of image is fed to a Gaussian fuzzy based decider to check whether the central pixel of image block needs correction. Hence, the central pixel of overlapped block is corrected by fuzzy gradient of its neighbors. Experimental results shows remarkable improvement with presented gFAR algorithm compared to the past methods subjectively visual quality and objectively PSNR . Deepak Gambhir "Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33361.pdf Paper Url: https://www.ijtsrd.com/computer-science/multimedia/33361/gaussian-fuzzy-blocking-artifacts-removal-of-high-dct-compressed-images/deepak-gambhir
An Efficient Approach for Image Enhancement Based on Image Fusion with Retine...ijsrd.com
Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. Image fusion refers to a technique that combines the information from two or more images of a scene into a single fused image.The Algorithm uses Retinex theory and gamma correction to perform a better enhancement of images. The algorithm can efficiently combine the advantages of Retinex and Gamma correction improving both color constancy and intensity of image.
FINGERPRINTS IMAGE COMPRESSION BY WAVE ATOMScsandit
The document presents a study comparing fingerprint image compression using wavelets and wave atoms transforms. It finds that wave atoms transforms provide better performance than current wavelet-based standards like WSQ. Specifically:
- Wave atoms achieved higher PSNR values and compression ratios than wavelets when reconstructing images from a reduced number of coefficients.
- An algorithm was proposed using wave atom decomposition, non-uniform quantization, and entropy coding that achieved a compression ratio of 18 with a PSNR of 35.04 dB, outperforming the WSQ standard.
- Minutiae detection on original and reconstructed images showed wave atoms better preserved local fingerprint structures. Therefore, wave atoms are concluded to be more suitable than wavelets
This document discusses techniques for detecting digital image forgeries. It begins by defining different types of forgeries such as image retouching, splicing, and cloning. It then discusses mechanisms for forgery detection, distinguishing between active methods that embed hidden information in images and passive methods that analyze image traces. A key technique presented is using rotation angle estimation to detect cloned regions, with details on calculating variance to determine the rotation angle. The document concludes by presenting an algorithm for region duplication detection using hybrid wavelet transforms like DCT, Walsh, and Hadamard transforms.
Interactive graph cuts for optimal boundary and region segmentation of objects yzxvvv
This paper proposes a new technique for interactive segmentation of multi-dimensional (N-D) images. The technique allows a user to mark pixels as "object" or "background" to provide hard constraints. A cost function is defined that balances boundary and region properties to find a globally optimal segmentation satisfying the constraints. Graph cuts are used to efficiently compute the optimal segmentation. The technique handles isolated object and background regions and can segment N-D volumes.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Data-Driven Motion Estimation With Spatial AdaptationCSCJournals
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
This document discusses optimizing image convolution operations for GPUs using CUDA. It describes how to implement a separable convolution filter in two passes, one for rows and one for columns. This reduces redundant data loads compared to a naive single-pass implementation. The document also discusses techniques like loading multiple pixels per thread and padding thread blocks to achieve coalesced global memory accesses and avoid idle threads when processing boundary pixels. Overall, the key optimizations are using a separable filter, loading multiple pixels per thread, and padding for coalesced memory access.
VARIATION-FREE WATERMARKING TECHNIQUE BASED ON SCALE RELATIONSHIPcscpconf
Most watermark methods use pixel values or coefficients as the judgment condition to embed or
extract a watermark image. The variation of these values may lead to the inaccurate condition
such that an incorrect judgment has been laid out. To avoid this problem, we design a stable
judgment mechanism, in which the outcome will not be seriously influenced by the variation.
The principle of judgment depends on the scale relationship of two pixels. From the observation
of common signal processing operations, we can find that the pixel value of processed image
usually keeps stable unless an image has been manipulated by cropping attack or halftone
transformation. This can greatly help reduce the modification strength from image processing
operations. Experiment results show that the proposed method can resist various attacks and
keep the image quality friendly.
Performance Improvement of Vector Quantization with Bit-parallelism HardwareCSCJournals
Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.
This paper proposes a novel approach called R2P (Recomposition and Retargeting of Photographic Images) that can automatically alter the composition of an input source image to match a reference image, while also resizing the recomposed output image to fit the reference. The method first extracts foreground objects from the source and reference images. It then performs recomposition by solving a graph matching optimization to transfer the composition from the reference to the source. Finally, it jointly retargets the recomposed source image by optimizing a mesh warping technique to minimize distortion while fitting the size of the reference image. The approach requires no user input, pre-collected training data, or predetermined composition rules.
Image Restoration UsingNonlocally Centralized Sparse Representation and histo...IJERA Editor
Due to the degradation of observed image the noisy, blurred, distorted image can be occurred .To restore the image informationby conventional modelsmay not be accurate enough for faithful reconstruction of the original image. I propose the sparse representations to improve the performance of based image restoration. In this method the sparse coding noise is added for image restoration, due to this image restoration the sparse coefficients of original image can be detected. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, fordenoising the image here we use the histogram clipping method by using histogram based sparse representation to effectively reduce the noise and also implement the TMR filter for Quality image. Various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed algorithm.
Background Estimation Using Principal Component Analysis Based on Limited Mem...IJECEIAES
Given a video of 푀 frames of size ℎ × 푤. Background components of a video are the elements matrix which relative constant over 푀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 푤 × 푀) into 2 dimensions one (푁 × 푀), where 푁 is a linear array of size ℎ × 푤 . The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds).
Region duplication forgery detection in digital imagesRupesh Ambatwad
Region duplication or copy move forgery is a common type of tampering scheme carried out to create a fake image. The field on blind image forensics depends upon the authenticity of the digital image. As in copy move forgery the duplicated region belongs to the same image, the detection of tampering is complex as it does not leave a visual clue. But the tampering gives rise to glitches at pixel level
The document compares Gaussian filtering and bilateral filtering on RGB images. Gaussian filtering replaces each pixel with a weighted average of neighboring pixels, with weights determined by a Gaussian function of distance. This blurs the image while reducing high-frequency components. Bilateral filtering also considers pixel intensity differences, using a range Gaussian to give higher weight to similar pixels, thus preserving edges while smoothing. The effect of varying the spatial and range parameters is examined, showing bilateral filtering better retains edges through the additional range parameter.
This document describes an implementation of artistic style learning using a neural network approach. The algorithm is based on the VGG convolutional neural network and uses the NVidia CUDA platform to speed up computations. The implementation reconstructs content using loss functions on convolutional layer outputs and represents style using Gram matrices of filter correlations. A new image is generated by minimizing a combined loss of content and style. Experiments show improved results over the original algorithm by using softplus activations and average pooling.
The document provides an introduction to the Finite Element Method (FEM). It discusses that FEM is a numerical technique used to find approximate solutions to partial differential equations. It involves discretizing a continuous domain into smaller, separate elements. Properties are assumed to be constant within each element. Elements are connected at nodes and assembled to model the entire structure. The document outlines the history and development of FEM. It also discusses different numerical methods used in engineering analysis and highlights the advantages, disadvantages, and limitations of using FEM.
This document describes a project that uses photometric stereo to reconstruct 3D surfaces from images taken under different lighting conditions on a computer screen. Photometric stereo uses variations in pixel intensities across images to estimate surface normals and reconstruct the 3D shape. The project creates a MATLAB program that performs photometric stereo in real-time by flashing different light patterns on a screen and capturing images with a webcam. By using singular value decomposition, the program can reconstruct surfaces without knowing the exact positions of the light sources, overcoming a limitation of traditional photometric stereo. The reconstruction contains noise but demonstrates photometric stereo in a less controlled environment. Further work could explore tradeoffs of the method and improve efficiency.
Seed net automatic seed generation with deep reinforcement learning for robus...NAVER Engineering
본 논문에서는 interactive segmentation 문제를 풀기 위하여 deep reinforcement learning을 활용한 seed gereration 기법을 제안한다. Interactive segmentation 문제의 이슈 중 하나는 사용자의 개입을 최소화하는 것이다. 본 논문에서 제안하는 시스템이 사용자를 대신하여 인공적인 seed를 생성하게 된다. 사용자는 initial seed 정보만을 제공하면 된다. 우리는 optimal seed point 정의의 모호함으로 인해 supervised 기법을 사용하여 학습하기 어려운 점을 reinforcement learning 기법을 사용하여 극복하였다. Seed generation 문제에 맞도록 MDP를 정의하여 deep-q-network를 성공적으로 학습하였다. 우리는 MSRA10K 데이터셋에 대하여 학습을 진행하여 기존 segmentation 알고리즘의 부정확한 initial 결과 대비 우수한 성능을 보였다.
NEIGHBOUR LOCAL VARIABILITY FOR MULTIFOCUS IMAGES FUSIONsipij
This document presents a new method for fusing multi-focus images called the Neighbor Local Variability (NLV) method. The NLV method calculates the local variability of each pixel based on the quadratic difference between the pixel value and those of neighboring pixels. This variability is used to weight pixel values when fusing the images. The optimal size of the neighborhood considered depends on the variance and size of the blur filter, and can be estimated using a provided model. The NLV method is compared to other fusion methods and is shown to produce better results based on the Root Mean Square Error evaluation metric.
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Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Imagesijtsrd
A new artifact removal method as cascade of Gaussian fuzzy edge decider and fuzzy image correction is proposed. In this design, a highly compressed i.e. low bit rate image is considered. Here, each overlapped block of image is fed to a Gaussian fuzzy based decider to check whether the central pixel of image block needs correction. Hence, the central pixel of overlapped block is corrected by fuzzy gradient of its neighbors. Experimental results shows remarkable improvement with presented gFAR algorithm compared to the past methods subjectively visual quality and objectively PSNR . Deepak Gambhir "Gaussian Fuzzy Blocking Artifacts Removal of High DCT Compressed Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33361.pdf Paper Url: https://www.ijtsrd.com/computer-science/multimedia/33361/gaussian-fuzzy-blocking-artifacts-removal-of-high-dct-compressed-images/deepak-gambhir
An Efficient Approach for Image Enhancement Based on Image Fusion with Retine...ijsrd.com
Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. Image fusion refers to a technique that combines the information from two or more images of a scene into a single fused image.The Algorithm uses Retinex theory and gamma correction to perform a better enhancement of images. The algorithm can efficiently combine the advantages of Retinex and Gamma correction improving both color constancy and intensity of image.
FINGERPRINTS IMAGE COMPRESSION BY WAVE ATOMScsandit
The document presents a study comparing fingerprint image compression using wavelets and wave atoms transforms. It finds that wave atoms transforms provide better performance than current wavelet-based standards like WSQ. Specifically:
- Wave atoms achieved higher PSNR values and compression ratios than wavelets when reconstructing images from a reduced number of coefficients.
- An algorithm was proposed using wave atom decomposition, non-uniform quantization, and entropy coding that achieved a compression ratio of 18 with a PSNR of 35.04 dB, outperforming the WSQ standard.
- Minutiae detection on original and reconstructed images showed wave atoms better preserved local fingerprint structures. Therefore, wave atoms are concluded to be more suitable than wavelets
This document discusses techniques for detecting digital image forgeries. It begins by defining different types of forgeries such as image retouching, splicing, and cloning. It then discusses mechanisms for forgery detection, distinguishing between active methods that embed hidden information in images and passive methods that analyze image traces. A key technique presented is using rotation angle estimation to detect cloned regions, with details on calculating variance to determine the rotation angle. The document concludes by presenting an algorithm for region duplication detection using hybrid wavelet transforms like DCT, Walsh, and Hadamard transforms.
Interactive graph cuts for optimal boundary and region segmentation of objects yzxvvv
This paper proposes a new technique for interactive segmentation of multi-dimensional (N-D) images. The technique allows a user to mark pixels as "object" or "background" to provide hard constraints. A cost function is defined that balances boundary and region properties to find a globally optimal segmentation satisfying the constraints. Graph cuts are used to efficiently compute the optimal segmentation. The technique handles isolated object and background regions and can segment N-D volumes.
LOCAL DISTANCE AND DEMPSTER-DHAFER FOR MULTI-FOCUS IMAGE FUSION sipij
This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing them to results of other methods.
Copy-Rotate-Move Forgery Detection Based on Spatial DomainSondosFadl
we propose a method which is efficient and fast for detecting Copy-Move regions even when the copied region was undergone rotation modify in spatial domain.
Data-Driven Motion Estimation With Spatial AdaptationCSCJournals
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
This document discusses optimizing image convolution operations for GPUs using CUDA. It describes how to implement a separable convolution filter in two passes, one for rows and one for columns. This reduces redundant data loads compared to a naive single-pass implementation. The document also discusses techniques like loading multiple pixels per thread and padding thread blocks to achieve coalesced global memory accesses and avoid idle threads when processing boundary pixels. Overall, the key optimizations are using a separable filter, loading multiple pixels per thread, and padding for coalesced memory access.
VARIATION-FREE WATERMARKING TECHNIQUE BASED ON SCALE RELATIONSHIPcscpconf
Most watermark methods use pixel values or coefficients as the judgment condition to embed or
extract a watermark image. The variation of these values may lead to the inaccurate condition
such that an incorrect judgment has been laid out. To avoid this problem, we design a stable
judgment mechanism, in which the outcome will not be seriously influenced by the variation.
The principle of judgment depends on the scale relationship of two pixels. From the observation
of common signal processing operations, we can find that the pixel value of processed image
usually keeps stable unless an image has been manipulated by cropping attack or halftone
transformation. This can greatly help reduce the modification strength from image processing
operations. Experiment results show that the proposed method can resist various attacks and
keep the image quality friendly.
Performance Improvement of Vector Quantization with Bit-parallelism HardwareCSCJournals
Vector quantization is an elementary technique for image compression; however, searching for the nearest codeword in a codebook is time-consuming. In this work, we propose a hardware-based scheme by adopting bit-parallelism to prune unnecessary codewords. The new scheme uses a “Bit-mapped Look-up Table” to represent the positional information of the codewords. The lookup procedure can simply refer to the bitmaps to find the candidate codewords. Our simulation results further confirm the effectiveness of the proposed scheme.
This paper proposes a novel approach called R2P (Recomposition and Retargeting of Photographic Images) that can automatically alter the composition of an input source image to match a reference image, while also resizing the recomposed output image to fit the reference. The method first extracts foreground objects from the source and reference images. It then performs recomposition by solving a graph matching optimization to transfer the composition from the reference to the source. Finally, it jointly retargets the recomposed source image by optimizing a mesh warping technique to minimize distortion while fitting the size of the reference image. The approach requires no user input, pre-collected training data, or predetermined composition rules.
Image Restoration UsingNonlocally Centralized Sparse Representation and histo...IJERA Editor
Due to the degradation of observed image the noisy, blurred, distorted image can be occurred .To restore the image informationby conventional modelsmay not be accurate enough for faithful reconstruction of the original image. I propose the sparse representations to improve the performance of based image restoration. In this method the sparse coding noise is added for image restoration, due to this image restoration the sparse coefficients of original image can be detected. The so-called nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, fordenoising the image here we use the histogram clipping method by using histogram based sparse representation to effectively reduce the noise and also implement the TMR filter for Quality image. Various types of image restoration problems, including denoising, deblurring and super-resolution, validate the generality and state-of-the-art performance of the proposed algorithm.
A STUDY AND ANALYSIS OF DIFFERENT EDGE DETECTION TECHNIQUEScscpconf
In the first study [1], a combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map were used to perform image segmentation and edge detection
tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging
procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain accurate edge maps of our images without using watershed method. In this technique: We solved the problem of undesirable over segmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image. In the 2nd study level set methods are used for the implementation of curve/interface evolution under various forces. In the third study the main idea is to detect regions (objects) boundaries, to isolate and extract individual components from a medical image. This is done using an active contours to detect regions in a given image, based on techniques of curve evolution, Mumford–Shah functional for segmentation and level sets. Once we classified our images into different intensity regions based on Markov Random Field. Then we detect regions whose boundaries are not necessarily defined by gradient by minimize an energy of Mumford–Shah functional forsegmentation, where in the level set formulation, the problem becomes a mean-curvature which will stop on the desired boundary. The stopping term does not depend on the gradient of the image as in the classical active contour. The initial curve of level set can be anywhere in the image, and interior contours are automatically detected. The final image segmentation is one
closed boundary per actual region in the image.
Scaling Transform Methods For Compressing a 2D Graphical image acijjournal
This document summarizes a research paper that proposes using 2D scaling transformations to compress grayscale images. It begins by defining scaling and different types of scaling transformations. It then describes how to represent 2D scaling mathematically using transformation matrices. The paper applies 2D scaling with different factors to compress the Lena test image and evaluates the compressed images using PSNR and MSE metrics. Scaling by factors of 2, 4, and 8 are tested, with higher scaling factors achieving better compression but lower image quality. In conclusions, the paper finds the proposed 2D scaling technique provides comparable or better performance than other image transformation methods for compression.
6 superpixels using morphology for rock imageAlok Padole
The document describes a new superpixel segmentation algorithm called Superpixels Using Morphology (SUM) and compares it to existing algorithms. SUM uses a watershed transformation approach on an image's morphological gradient that has undergone area closing to efficiently generate superpixels. In experiments on rock images, SUM achieved under-segmentation error and boundary recall comparable to recent algorithms while being significantly faster, making it suitable for applications requiring fast superpixel generation.
An image can be seen as a matrix I, where I(x, y) is the brightness of the pixel located at coordinates (x, y). In the Convolutional neural network, the kernel is nothing but a filter
that is used to extract the features from the images.
Image segmentation Based on Chan-Vese Active Contours using Finite Difference...ijsrd.com
There are a lot of image segmentation techniques that try to differentiate between backgrounds and object pixels but many of them fail to discriminate between different objects that are close to each other, e.g. low contrast between foreground and background regions increase the difficulty for segmenting images. So we introduced the Chan-Vese active contours model for image segmentation to detect the objects in given image, which is built based on techniques of curve evolution and level set method. The Chan-Vese model is a special case of Mumford-Shah functional for segmentation and level sets. It differs from other active contour models in that it is not edge dependent, therefore it is more capable of detecting objects whose boundaries may not be defined by a gradient. Finally, we developed code in Matlab 7.8 for solving resulting Partial differential equation numerically by the finite differences schemes on pixel-by-pixel domain.
Scaling Transform Methods For Compressing a 2D Graphical imageacijjournal
Transformation is a process of converting the original picture coordinates into a different picture
coordinates either by adding some values with original coordinates(Translations) or Multiplying some
values with original coordinates(called Linear transformations like rotation, reflection, scaling, and
shearing). In this paper, we compress a two dimensional picture using 2D scaling transformation. In the
several scenarios, the utilization of this technique for image compression resulted in comparable or better
performance, when compared to the Different modes of image transformations. In this paper We tried a
new code for compressing an 2d gray scale image using scaling transform methods. Matlab concepts are
applied to compress the image. We have plan to apply The techniques and develop a code for compressing
a 3d image.
This document discusses image segmentation techniques. It begins by introducing the goal of image segmentation as clustering pixels into salient image regions. Segmentation can be used for tasks like object recognition, image compression, and image editing. The document then discusses several bottom-up image segmentation approaches, including clustering pixels in feature space using mixtures of Gaussians models or K-means, mean-shift segmentation which models feature density non-parametrically, and graph-based segmentation methods which construct similarity graphs between pixels. It provides examples and discusses assumptions and limitations of each approach. The key approaches discussed are clustering in feature space, mean-shift segmentation, and graph-based similarity methods like the local variation algorithm.
The document summarizes a research paper that uses a technique called deconvnet to visualize and understand what convolutional neural networks have learned. It introduces deconvnet as a method to approximate activations in higher layers of a convnet by using transposed convolutions and max location switches from pooling layers. The document then shows examples of visualizing filters from different layers of a trained convnet on ImageNet, revealing what patterns and parts of images the network has learned to detect at each layer.
This paper proposes a method to jointly match multiple 3D meshes by maximizing pairwise feature affinities and cycle consistency across models. It formulates the matching problem as a low-rank matrix recovery problem and uses nuclear norm relaxation for rank minimization. An alternating minimization algorithm is used to efficiently solve the optimization problem. Experimental results show the method provides an order of magnitude speed-up compared to state-of-the-art algorithms based on semi-definite programming, while achieving competitive performance. It also introduces a distortion term to the pairwise matching to help match reflexive sub-parts of models distinctly.
Joint3DShapeMatching - a fast approach to 3D model matching using MatchALS 3...Mamoon Ismail Khalid
we extend the global optimization-based
approach of jointly matching a set of images to jointly
matching a set of 3D meshes. The estimated correspon
dences simultaneously maximize pairwise feature affini
ties and cycle consistency across multiple models. We
show that the low-rank matrix recovery problem can be
efficiently applied to the 3D meshes as well. The fast
alternating minimization algorithm helps to handle real
world practical problems with thousands of features. Ex
perimental results show that, unlike the state-of-the-art
algorithm which rely on semi-definite programming, our
algorithm provides an order of magnitude speed-up along
with competitive performance. Along with the joint shape
matching we propose an approach to apply a distortion
term in pairwise matching, which helps in successfully
matching the reflexive sub-parts of two models distinc
tively. In the end, we demonstrate the applicability of
the algorithm to match a set of 3D meshes of the SCAPE
benchmark database
Neighbour Local Variability for Multi-Focus Images Fusionsipij
The goal of multi-focus image fusion is to integrate images with different focus objects in order to obtain a
single image with all focus objects. In this paper, we give a new method based on neighbour local
variability (NLV) to fuse multi-focus images. At each pixel, the method uses the local variability calculated
from the quadratic difference between the value of the pixel and the value of all pixels in its
neighbourhood. It expresses the behaviour of the pixel with respect to its neighbours. The variability
preserves the edge function because it detects the sharp intensity of the image. The proposed fusion of each
pixel consists of weighting each pixel by the exponential of its local variability. The quality of this fusion
depends on the size of the neighbourhood region considered. The size depends on the variance and the size
of the blur filter. We start by modelling the value of the neighbourhood region size as a function of the
variance and the size of the blur filter. We compare our method to other methods given in the literature.
We show that our method gives a better result.
This document discusses computer vision and robot vision. It describes early work using artificial neural networks to allow a robot to steer a vehicle based on camera images (ALVINN system). The document outlines the two main stages of robot vision: image processing and scene analysis. Image processing transforms raw images, e.g. through averaging, edge enhancement, and region finding algorithms. Scene analysis extracts task-specific information by interpreting lines, curves, and applying model-based approaches to reconstruct scenes from primitive 3D objects. Stereo vision obtains depth information through triangulation using two camera images.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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Similar to Qcce quality constrained co saliency estimation for common object detection (20)
In this paper, we present how Bell’s Palsy, a neurological disorder, can be detected just from a subject’s eyes in a video. We notice that Bell’s Palsy patients often struggle to blink their eyes on the affected side. As a result, we can observe a clear contrast between the blinking patterns of the two eyes. Although previous works did utilize images/videos to detect this disorder, none have explicitly focused on the eyes. Most of them require the entire face. One obvious advantage of having an eye-focused detection system is that subjects’ anonymity is not at risk. Also, our AI decisions are based on simple blinking patterns, making them explainable and straightforward. Specifically, we develop a novel feature called blink similarity, which measures the similarity between the two blinking patterns. Our extensive experiments demonstrate that the proposed feature is quite robust, for it helps in Bell’s Palsy detection even with very few labels. Our proposed eye-focused detection system is not only cheaper but also more convenient than several existing methods.
Most existing high-performance co-segmentation algorithms
are usually complex due to the way of co-labelling a
set of images as well as the common need of fine-tuning few
parameters for effective co-segmentation. In this paper, instead
of following the conventional way of co-labelling multiple images,
we propose to first exploit inter-image information through cosaliency,
and then perform single-image segmentation on each
individual image. To make the system robust and to avoid heavy
dependence on one single saliency extraction method, we propose
to apply multiple existing saliency extraction methods on each
image to obtain diverse salient maps. Our major contribution lies
in the proposed method that fuses the obtained diverse saliency
maps by exploiting the inter-image information, which we call
saliency co-fusion. Experiments on five benchmark datasets with
eight saliency extraction methods show that our saliency co-fusion
based approach achieves competitive performance even without
parameter fine-tuning when compared with the state-of-the-art
methods.
This document summarizes a project report on image segmentation using an advanced fuzzy c-means algorithm. The report was submitted by two students to fulfill requirements for a Bachelor of Technology degree in Electrical Engineering at the Indian Institute of Technology Roorkee. It describes implementing various clustering algorithms for image segmentation, including k-means, fuzzy c-means, bias-corrected fuzzy c-means, and Gaussian kernel fuzzy c-means. It then proposes improvements to the algorithms by automatically selecting the number of clusters and initial cluster centers based on a moving average filter on the image histogram. This approach removes problems of non-convergence and increases speed, enabling real-time video segmentation.
This document provides an overview of machine learning and different types of machine learning algorithms. It discusses how machine learning algorithms have been used to rank webpages, filter spam emails, and make predictions. The document explains that machine learning grew out of artificial intelligence research and allows computers to learn without being explicitly programmed. It describes supervised learning algorithms, which are taught using labeled training data, and unsupervised learning algorithms, which find hidden patterns in unlabeled data. Examples of supervised learning problems include regression to predict prices and classification to detect cancer. Unsupervised learning finds clusters within data without predefined labels.
Most existing high-performance co-segmentation algorithms are usually complicated due to the way of co-labelling a set of images and the requirement to handle quite a few parameters for effective co-segmentation. In this paper, instead of relying on the complex process of co-labelling multiple images, we perform segmentation on individual images but based on a combined saliency map that is obtained by fusing single-image saliency maps of a group of similar images. Particularly, a new multiple image based saliency map extraction, namely geometric mean saliency (GMS) method, is proposed to obtain the global saliency maps. In GMS, we transmit the saliency information among the images using the warping technique. Experiments show that our method is able to outperform state-of-the-art methods on three benchmark co-segmentation datasets.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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Qcce quality constrained co saliency estimation for common object detection
1. QCCE: Quality Constrained Co-saliency Estimation
for Common Object Detection
Koteswar Rao Jerripothula #∗§1
, Jianfei Cai ∗§2
, Junsong Yuan †§3
#
Interdisciplinary Graduate School, ∗
School of Computer Engineering, †
School of Electrical and Electronic Engineering
§
Nanyang Technological University, Singapore. email:1
KOTESWAR001@e.ntu.edu.sg, {2
ASJFCAI,3
JSYUAN}@ntu.edu.sg
Abstract—Despite recent advances in joint processing of im-
ages, sometimes it may not be as effective as single image
processing for object discovery problems. In this paper while
aiming for common object detection, we attempt to address
this problem by proposing a novel QCCE: Quality Constrained
Co-saliency Estimation method. The approach here is to itera-
tively update the saliency maps through co-saliency estimation
depending upon quality scores, which indicate the degree of
separation of foreground and background likelihoods (the easier
the separation, the higher the quality of saliency map). In this
way, joint processing is automatically constrained by the quality
of saliency maps. Moreover, the proposed method can be applied
to both unsupervised and supervised scenarios, unlike other
methods which are particularly designed for one scenario only.
Experimental results demonstrate superior performance of the
proposed method compared to the state-of-the-art methods.
Index Terms—quality, co-saliency, co-localization, bounding-
box, propagation, object detection.
I. INTRODUCTION
Object detection has many applications since it facilitates
efficient utilization of computational resources exclusively on
the region of interest. Saliency is a common cue used in object
detection, but it has only obtained limited success when images
have cluttered background. Recent progress in joint processing
of images like co-segmentation [1][2][3], co-localization [4],
knowledge transfer [5][6][7] has been quite effective in this
regard because of the ability to exploit commonness which
cannot be done in single image processing.
Despite previous progress, there still exist some major
problems for the existing joint processing algorithms. 1) As
shown in [1][2], joint processing of images might not perform
better than single-image processing for some datasets. This
raises up the question: to process jointly or not. 2) Most of
the existing high-performance joint processing algorithms are
usually complicated due to the way of co-labelling the pixels
[2] or co-selection of boxes [4] in a set of images, and also
require to tune parameters for effective co-segmentation or co-
localization, which becomes much more difficult when dataset
becomes increasingly diverse.
There are two types of common object detection: 1) Super-
vised [6][7], where the task is to populate entire dataset with
the help of some available bounding boxes; 2) Unsupervised
[4], where the task is to populate entire dataset without any
partial labels. In this paper, we handle both types in one
framework. Our approach is to iteratively update saliency maps
using co-saliency estimation while measuring their quality. For
a high-quality saliency map, its foreground and background
should be easily separated. Therefore, simple images with a
clear background and foreground separation may not need the
help from joint processing. For complex images with cluttered
backgrounds, by iteratively updating the saliency maps through
co-saliency estimation, we are able to gradually improve the
saliency maps although they did not have high-quality saliency
map to begin with. Images with high-quality saliency maps can
play the leading role in the co-saliency estimation of other
images. Moreover, some images may already have ground-
truth bounding boxes. In such cases, the bounding boxes can
replace respective saliency maps as the high-quality saliency
maps to help generate better co-saliency maps. Since saliency
maps are updated iteratively through co-saliency estimation
constrained by their quality scores, we call it QCCE: Quality
Constrained Co-saliency Estimation. The advantage of such an
approach is twofold: (1) It can work effectively for big image
dataset and can benefit from high-quality saliency maps; (2) It
can automatically choose either the original saliency map or
the jointly processed saliency map.
Assuming a Gaussian distribution for foreground and back-
ground likelihoods in the saliency map, we make use of
the overlap between the two distributions to calculate quality
scores. We employ co-saliency estimation [3] along with our
quality scores to update the saliency maps. The foreground
likelihood of the high-quality saliency map is used as a rough
common object segment to define bounding boxes eventually.
Prior to our work, both co-localization [4] problem and
bounding box propagation problem [6][7] have been studied on
challenging datasets such as ImageNet. While [4] suffers from
low accuracy and [6][7] essentially depend upon bounding
box availability. In contrast, the proposed method can not only
address both problems, but also outperform the existing works.
II. PROPOSED METHOD
Our goal is to define bounding boxes around the common
objects in a set of similar images. High-quality saliency maps
are obtained while measuring the quality of saliency maps
that are iteratively updated via co-saliency estimation. These
high-quality saliency maps are then used to eventually define
2. Overlap (Oi
k )
*
z
O Foreground Class
* Background Class
z (Saliency values)
Numberofpixels X 100
Fi
k1
Fi
k0
Fig. 1. Quality of saliency map is measured using overlap of estimated
distribution of the two classes: Foreground and Background
bounding boxes. In this section, we provide details of quality
scores, co-saliency estimation and defining bounding boxes.
A. Notation
Let I = {I1, I2, · · · , Im} be the image set containing
m images and Di be the pixel domain of Ii. Let set of
saliency maps be denoted as S = {S1, S2, · · · , Sm} and
set of their corresponding quality scores be denoted as
Q = {Q1, Q2, · · · , Qm}. For images already having bounding
boxes, saliency maps are replaced by respective bounding
boxes and their quality scores are set as 1.
B. Quality Score
By quality, we mean how easily two likelihoods (foreground
and background) are separable. These likelihoods are formed
by thresholding saliency map using the Otsu method. Based on
such classification, let µk1
i , µk0
i , σk1
i and σk0
i be foreground
mean, background mean, foreground standard deviation and
background standard deviation for saliency map Si at the kth
iteration (denoted as Sk
i ), respectively.
Assuming Gaussian distribution for both likelihoods, we
denote foreground and background distributions as Fk1
i (z) and
Fk0
i (z), respectively, where z is the saliency value ranging
between 0 and 1.
It is clear that the less the two distributions overlap with
each other, the better the saliency map is, i.e., the foreground
and background are more likely to be separable. In order to
calculate the overlap, it is needed to figure out the intersecting
point (see Fig. 1). It can be obtained by equating the two
functions, i.e. Fk1
i (z) = Fk0
i (z), which leads to:
z2
(
1
(σk0
i )2
−
1
(σk1
i )2
) − 2z(
µk0
i
(σk0
i )2
−
µk1
i
(σk1
i )2
) +
(µk0
i )2
(σk0
i )2
−
(µk1
i )2
(σk1
i )2
+ 2 log(σk0
i ) − 2 log(σk1
i ) = 0 (1)
Let the solution of the above quadratic equation be z∗
and
the overlap (O) can now be computed as
Ok
i =
z=z∗
z=0
Fk1
i (z) +
z=1
z=z∗
Fk0
i (z) (2)
0.999 0.774 0.623 0.541
0.504 0.421 0.416 0.365
Fig. 2. Sample Images with their saliency maps and quality scores. Saliency
maps with low-quality score fail to highlight the starfish.
where Ok
i represents the overlap of two classes in Si at the
kth
iteration.
Finally, quality score Qi for kth
iteration (denoted as Qk
i )
is calculated as,
Qk
i =
1
1 + log10 (1 + Ok
i )
(3)
As we keep updating saliency maps through interaction with
other images, we want to choose high-quality saliency maps,
i.e. for which maximum quality score is obtained. In Fig. 2,
we show a set of images with their saliency maps and quality
scores. It can be seen that saliency maps become unfit to
highlight the starfish as quality score decreases from top-left
to bottom-right.
C. Co-saliency Estimation
The way we update saliency maps after each iteration is
through co-saliency estimation which can boost saliency of the
common object and suppress background saliency. In order
to avoid large variation across images while developing co-
saliency maps, in each iteration k we cluster the images into
sub-groups by k-means with the weighted GIST feature [2]
where saliency maps are used for weights. Let Gv be the set
of indexes (i of Ii) of images in the vth
cluster.
We adopt the idea of co-saliency estimation from [3] where
the geometric mean of the saliency map of one image and
warped saliency maps of its neighbor images is taken as the
co-saliency map. However, we make a slight modification to
suit our model, i.e. we use the weighted mean function instead
of the geometric mean where weights are our quality scores.
Saliency Enhancement via Warping: Basically, saliency
enhancement takes place at pixel level amongst correspond-
ing pixels. Specifically, following [2], masked Dense SIFT
correspondence [8] is used to find corresponding pixels in
each image pair. Masks here are the label maps obtained by
thresholding the saliency maps. This ensures that pixels having
high foreground likelihood play the dominant role in guiding
the SIFT flow. The energy function for Dense SIFT flow can
3. now be represented as
E(wk
ij; Sk
i , Sk
j ) =
p∈Di
φ Sk
i (p)
φ Sk
j (p + wk
ij(p)) ||Ri(p) − Rj(p + wk
ij(p))||1 + 1−
φ Sk
j (p + wk
ij(p)) B0 +
q∈Ni
p
α||wk
ij(p) − wk
ij(q)||2 (4)
where Ri is dense SIFT feature descriptor for image Ii. The
likelihood function φ for saliency map gives class labels:1 (for
foreground likelihood) or 0 (for background likelihood). It can
be seen how feature difference is masked by the likelihoods of
involved pixels. B0 is a large constant which ensures large cost
if the potential corresponding pixel in another image happens
to have background likelihood. Weighted by another constant
α and likelihood, neighbourhood Ni
p of pixel p is considered
for smooth flow field wij from image Ii to Ij.
Updating Saliency Maps: Given a pair of images Ii and Ij
from a subgroup Gv, we form the warped saliency map Uji
by Uji(p) = Sk
j (p ), where (p, p ) is a matched pair in the
SIFT flow alignment with relationship p = p + wij(p). Since
there are quite a few images in subgroup Gv, for image Ii,
we may update its saliency map by computing the weighted
mean where weights are respective quality scores, i.e.
Sk+1
i (p) =
|Sk
i (p)|Qk
i +
j∈Gv
j=i |Uk
ji(p)|Qk
j
j∈Gv
Qk
j
(5)
This kind of weights ensures that high-quality saliency maps
play the leading role in the development of new saliency
maps so that new saliency maps evolve towards better ones.
Moreover, we also take advantage of prior bounding boxes
available which are of high-quality right from the beginning.
D. Convergency and Bounding Box Generation
Convergency: If an image reaches its high-quality saliency
map, updating of saliency map should stop. Thus, saliency
maps of images with ground-truth bounding boxes as high-
quality saliency maps get never updated, whereas the saliency
maps of other images may get updated depending upon the
quality scores. If quality score decreases in next iteration or
difference is very small, say 0.005, we will not update the
saliency map of the image and proceed for bounding box
generation.
Bounding Box Generation: Since rough segmentation
itself can help developing bounding box, we consider fore-
ground and background likelihoods themselves as common
foreground segment and background segment respectively. We
get a number of potential sparsely located group of white pix-
els as objects or connected components using bwconncomp()
function of MATLAB which we denote as c. In order to
avoid noisy insignificant objects, we develop an object saliency
density metric for each of these objects assuming that real
objects would have high foreground saliency density and low
background saliency density (here background is rest of the
pixel domain). Therefore saliency density metric is defined as:
ImageNet
Internet
Fig. 3. Sample Results from ImageNet and Internet datasets
V (c) =
p∈c Si(p)
|c|
−
p∈¯c Si(p)
|¯c|
(6)
where ¯c is the set of the rest of the pixels and |c| is the number
of pixels in object c. Objects with high saliency density metric
are likely to be the real objects. For an image, only those
object(s) which are ≥ 50 percentile according to this object
saliency density metric V or is the only object in the image
are considered for developing bounding boxes. The bounding
box is then drawn using topmost, bottommost, leftmost and
rightmost boundary pixels of the qualified objects.
III. EXPERIMENTAL RESULTS
As per our claim that the proposed method can work
better in both unsupervised and supervised scenarios, we
use same large scale experimental set up as [4] and [7]
for co-localization and bounding box propagation problems,
respectively. Following [4], we use CorLoc evaluation metric,
i.e., percentage of images that satisfy the condition IOU
(intersection over union) defined as
area(BBgt∩BBpr)
area((BBgt∪BBpr) >
0.5 where BBgt and BBpr are ground-truth and proposed
bounding boxes maps, respectively. To distinguish between
supervised results and unsupervised results, suffixes (S) and
(U) are used, respectively. We use the saliency maps of [9] and
[10] in co-localization and bounding box propagation setups,
4. TABLE I
CORLOC COMPARISON ON IMAGENET AND INTERNET IMAGES DATASET
IN CO-LOCALIZATION SETUP
ImageNet Internet
Baseline using [9](U) 52.9 65.6
[2](U) - 75.2
[4](U) 53.2 76.6
Proposed Method(U) 64.3 82.8
TABLE II
COMPARISON ON IMAGENET IN BBP SETUP
CorLoc
Baseline using [10](U) 64.9
[6](S) 58.5
[7](S) 66.5
[7]∗(S) 68.3
Proposed Method(S) 70.9
Proposed Method(U) 68.6
respectively. We use bounding boxes generated from these
initial saliency maps as baselines.
Co-localization Setup: As per the setup in [4], there are 1
million images for which bounding boxes are available on the
ImageNet spread over 3627 classes. In addition to ImageNet,
for Internet [2] dataset which is actually segmentation dataset,
a tight bounding box is developed across each foreground
segment and is used as ground truth. We compare our results
in Table I for both datasets. It can be seen that we obtain
superior results with margin of 11% and 6% improvements on
these datasets, respectively. Moreover, our results are obtained
without any parameter tuning whereas both [4] and [2] have
tuned their parameters on Internet dataset.
Bounding Box Propagation Setup: We would like to
acknowledge [7] for providing the list of test and training
images upon request. In this setup, 32k images are considered
for testing purposes, and the saliency maps of the training
images of the classes, to which these test images belong,
are replaced with bounding boxes. The problem that we are
trying to address is similar to “Self” case in [7] where only
images within the same class are used for training. In Table
II, we compare our results on these 32k test images with two
previous attempts in [6][7] to populate ImagNet with bounding
boxes in such a supervised manner. [7]∗
refers to their results
using state-of-the-art features and object proposals. Our results
are 4% better than state-of-the-art [7]∗
(S). Considering the
proposed method does not essentially need bounding boxes,
we report our unsupervised results (Proposed Method(U)) of
these 32k test images as well where we do not use any initial
bounding boxes and still obtain comparable results to [7]∗
(S).
Fig. 3 shows sample results obtained on ImageNet and
Internet datasets. In addition, we show our results along with
ground-truth for visual comparison in Fig. 4. It can be seen
that proposed method is able to accurately provide bounding
boxes for both simple and complex images because we are
able to effectively constrain the joint processing of images.
IV. CONCLUSION AND FUTURE WORK
We have proposed a QCCE method for common object
detection. In the process, we try to address the critical issue
Fig. 4. Sample Visual Comparison between Ground Truth (Green) and Our
Results (Red)
of whether to process jointly or not with the help of constraint
on the quality of saliency maps. QCCE can act in both
supervised and unsupervised ways and obtains superior results
in both scenarios. Moreover, it can be well extended to co-
segmentation problem. Future work includes incorporating
more considerations in the development of quality scores.
ACKNOWLEDGMENT
This research was carried out at the Rapid-Rich Object
Search (ROSE) Lab at the Nanyang Technological University,
Singapore. The ROSE Lab is supported by the National
Research Foundation, Singapore, under its Interactive Digital
Media (IDM) Strategic Research Programme.
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