Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while sema...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computervision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.
The proceedings contains 165 papers on computervision and patternrecognition. Topics discussed include recognition systems, image processing, computational methods, algorithms and information use.
ISBN:
(纸本)0818658274
The proceedings contains 165 papers on computervision and patternrecognition. Topics discussed include recognition systems, image processing, computational methods, algorithms and information use.
Camouflaged object detection (COD), the task of identifying objects concealed within their surroundings, is often quite challenging due to the similarity that exists between the foreground and background. By incorpora...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Camouflaged object detection (COD), the task of identifying objects concealed within their surroundings, is often quite challenging due to the similarity that exists between the foreground and background. By incorporating an additional referring image where the target object is clearly visible, we can leverage the similarities between the two images to detect the camouflaged object. In this paper, we propose a novel problem setup: referring camouflaged object discovery (RCOD). In RCOD, segmentation occurs only when the object in the referring image is also present in the camouflaged image; otherwise, a blank mask is returned. This setup is particularly valuable when searching for specific camouflaged objects. Current COD methods are often generic, leading to numerous false positives in applications focused on specific objects. To address this, we introduce a new framework called Co-Saliency Inspired Referring Camouflaged Object Discovery (CIRCOD). Our approach consists of two main components: Co-Saliency-Aware Image Transformation (CAIT) and Co-Salient Object Discovery (CSOD). The CAIT module reduces the appearance and structural variations between the camouflaged and referring images, while the CSOD module utilizes the similarities between them to segment the camouflaged object, provided the images are semantically similar. Covering all semantic categories in current COD benchmark datasets, we collected over 1,000 referring images to validate our approach. Our extensive experiments demonstrate the effectiveness of our method and show that it achieves superior results compared to existing methods. Code is available at https://***/avigupta2798/CIRCOD/.
Line segment detection is a fundamental procedure in computervision, patternrecognition, and image analysis applications. The paper proposes a novel method for wide line segment detection especially endpoints determ...
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ISBN:
(数字)9798331506520
ISBN:
(纸本)9798331506537
Line segment detection is a fundamental procedure in computervision, patternrecognition, and image analysis applications. The paper proposes a novel method for wide line segment detection especially endpoints determination based on the Guided Scale Space Radon Transform and Hessian orientations. The method begins by determining the centerlines of wide lines and then exploit the image Hessian orientations around these lines to define binary region support of the line segments and then detect endpoints. The method shows to be robust against blur and noise on synthetic images where, the evaluation of the outcomes reveals the correctness of the detection by achieving low errors. In addition, results on real images are very promising.
The proceedings contains 144 papers. Topics discussed include motion tracking, face detection and recognition, pattern analysis, two dimensional and low-level vision, real time systems, computervision, medical image ...
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The proceedings contains 144 papers. Topics discussed include motion tracking, face detection and recognition, pattern analysis, two dimensional and low-level vision, real time systems, computervision, medical image analysis, deformable models and shape, video imaging, shape analysis, video libraries, physics-based vision, face recognition, computer graphics and patternrecognition.
The proceedings contains 158 papers from 2001 ieeecomputer society conference on computervision and patternrecognition. The topics discussed include: image indexing, image segmentation, computervision, image codin...
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The proceedings contains 158 papers from 2001 ieeecomputer society conference on computervision and patternrecognition. The topics discussed include: image indexing, image segmentation, computervision, image coding, patternrecognition systems, image magnification, video inpainting, visual tracking, motion estimation, face recognition, imaging systems, character recognition and feature clustering.
This Volume 1 of 2 of the conference proceedings contains 117 papers. Topics discussed include stereo vision, tracking, image retrieval, illumination, faces, shape and segmentation, shape pattern and statistics, motio...
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This Volume 1 of 2 of the conference proceedings contains 117 papers. Topics discussed include stereo vision, tracking, image retrieval, illumination, faces, shape and segmentation, shape pattern and statistics, motion and motion rendering, color and light, matching and recognition.
Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object pre...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net.
The proceedings contains 137 papers. Topics discussed include image segmentation and restoration, object recognition and pose recovery, human motion and articulated motion, target recognition, contour recognition, act...
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The proceedings contains 137 papers. Topics discussed include image segmentation and restoration, object recognition and pose recovery, human motion and articulated motion, target recognition, contour recognition, active and real-time vision, face detection and recognition, graph matching and calibration, shape representation, motion estimation and structure, stereo matching, texture and shading, image database and document analysis, feature extraction and detection, patternrecognition, low level vision.
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