- 标题
- 摘要
- 关键词
- 实验方案
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Linking luminance and lightness by global contrast normalization
摘要： In the present experiment we addressed the question of how the visual system determines surface lightness from luminances in the retinal image. We measured the perceived lightness of target surfaces that were embedded in custom-made checkerboards. The checkerboards consisted of 10 by 10 checks of 10 different reflectance values that were arranged randomly across the board. They were rendered under six viewing conditions including plain view, with a shadow-casting cylinder, or with one of four different transparent media covering part of the board. For each reflectance we measured its corresponding luminance in the different viewing conditions. We then assessed the lightness matches of four observers for each of the reflectances in the different viewing conditions. We derived predictions of perceived lightness based on local luminance, Michelson contrast, edge integration, anchoring theory, and a normalized Michelson contrast measure. The normalized contrast measure was the best predictor of surface lightness and was almost as good as the actual reflectance values. The normalized contrast measure combines a local computation of Michelson contrast with a region-based normalization of contrast ranges with respect to the contrast range in plain view. How the segregation of image regions is accomplished remains to be elucidated.
关键词： lightness/brightness perception,contrast,segmentation,surface reflectance
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11257 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II) || GAN and DCN Based Multi-step Supervised Learning for Image Semantic Segmentation
摘要： Image semantic segmentation contains two sub-tasks, segmenting and labeling. However, the recent fully convolutional network (FCN) based methods often ignore the first sub-task and consider it as a direct labeling one. Even though these methods have achieved competitive performances, they obtained spatially fragmented and disconnected outputs. The reason is that, pixel-level relationships inside the deepest layers become inconsistent since traditional FCNs do not have any explicit pixel grouping mechanism. To address this problem, a multi-step supervised learning method, which contains image-level supervised learning step and pixel-level supervised learning step, is proposed. Specifically, as for the visualized result of image semantic segmentation, it is actually an image-to-image transformation problem, from RGB domain to category label domain. The recent conditional generative adversarial network (cGAN) has achieved significant performance for image-to-image generation task, and the generated image remains good regional connectivity. Therefore, a cGAN supervised by RGB-category label map is used to obtain a coarse segmentation mask, which avoids generating disconnected segmentation results to a certain extent. Furthermore, an interaction information (II) loss term is proposed for cGAN to remain the spatial structure of the segmentation mask. Additionally, dilated convolutional networks (DCNs) have achieved significant performance in object detection field, especially for small objects because of its special receptive field settings. Specific to image semantic segmentation, if each pixel is seen as an object, this task can be transformed to object detection. In this case, combined with the segmentation mask from cGAN, a DCN supervised by the pixel-level label is used to finalize the category recognition of each pixel in the image. The proposed method achieves satisfactory performances on three public and challenging datasets for image semantic segmentation.
关键词： cGAN,Multi-step supervised learning,DCN,Image semantic segmentation
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis Volume 11043 (7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings) || Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Images
摘要： Bioresorbable Vascular Sca?old (BVS) is the latest stent type for the treatment of coronary artery disease. A major challenge of BVS is that once it is malapposed during implantation, it may potentially increase the risks of late stent thrombosis. Therefore it is important to analyze struts malapposition during implantation. This paper presents an automatic method for BVS malapposition analysis in intravascular optical coherence tomography images. Struts are ?rstly detected by a detector trained through deep learning. Then, struts boundaries are segmented using dynamic programming. Based on the segmentation, apposed and malapposed struts are discriminated automatically. Experimental results show that the proposed method successfully detected 97.7% of 4029 BVS struts with 2.41% false positives. The average Dice coe?cient between the segmented struts and ground truth was 0.809. It concludes that the proposed method is accurate and e?cient for BVS struts detection and segmentation, and enables automatic malapposition analysis.
关键词： Bioresorbable vascular sca?old,Deep learning,Intravascular optical coherence tomography,Detection and segmentation
更新于2025-09-04 15:30:14
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Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
摘要： Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
关键词： Superpixel segmentation,Hyperspectral images,Denoising,PSSV
更新于2025-09-04 15:30:14
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Imaging and visualization of the polarization state of the probing beam in polarization-sensitive optical coherence tomography
摘要： We propose a simple and ef?cient method of color-encoded Stokes parameters to visualize the polarization states for polarization-sensitive optical coherence tomography (PS-OCT) of biological tissue. In this method, polarization states of the probing light are detected and described in the Stokes domain. Three primary colors of red, green, and blue are used to code Stokes parameters of Q, U, and V, respectively, which can be used to represent and visualize each unique polarization state. A strategy that uses the polarization state as the PS-OCT imaging parameter is ?rst introduced to obtain high contrast images of the birefringent samples. Then, color-based automatic segmentation of birefringent components from 3D scanned tissue volume is proposed to isolate the network of the 3D nerve bundles in a mouse brain without cutting the brain into slices. Experimental validation and demonstrations are given by imaging ex vivo mouse tail and brain tissues to show the usefulness of proposed polarization state imaging and segmentation methods.
关键词： automatic segmentation,Stokes parameters,birefringent tissue,polarization-sensitive optical coherence tomography,polarization state imaging
更新于2025-09-04 15:30:14
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Image Judgment Auxiliary System for Table Tennis Umpiring under Low Light Conditions
摘要： In table tennis competitions, the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. It is often di?cult to provide enough light when using high-speed photography and this leads to underexposure. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can ?nd the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.
关键词： Table tennis,low light source,automatic tracking,hue-saturation-value (HSV) image segmentation
更新于2025-09-04 15:30:14
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation
摘要： Semantic image segmentation, which assigns labels in pixel level, plays a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning. However, one central problem of these methods is that deep convolutional neural network gives little consideration to the correlation among pixels. To handle this issue, in this paper, we propose a novel deep neural network named RelationNet, which utilizes CNN and RNN to aggregate context information. Besides, a spatial correlation loss is applied to train RelationNet to align features of spatial pixels belonging to same category. Importantly, since it is expensive to obtain pixel-wise annotations, we exploit a new training method to combine the coarsely and finely labeled data. Experiments show the detailed improvements of each proposal. Experimental results demonstrate the effectiveness of our proposed method to the problem of semantic image segmentation, which obtains state-of-the-art performance on the Cityscapes benchmark and Pascal Context dataset.
关键词： Spatial correlation loss,CNN,Semantic image segmentation,RNN,Deep learning,RelationNet
更新于2025-09-04 15:30:14
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Digital Image Correlation for discontinuous displacement measurement using subset segmentation
摘要： Deformation measurement is normally achieved by using Digital Image Correlation (DIC) technique when deformation is not discontinuous. However, the presence of discontinuities makes the deformation process very challenging and DIC fails. An innovative technique is proposed in this study which splits the subset (segment) of an image into multiple parts and use segmented subset of the image for correlation process. The performance of the proposed technique is evaluated using di?erent experiments where di?erent types of discontinuities are introduced in the deformation process at di?erent angles and having di?erent discontinuity opening sizes. The obtained results are compared with the recently proposed Discontinuous Digital Image Correlation (DDIC) technique. The results show that the proposed technique is more reliable and having high accuracy which reaches upto 1/100th of a pixel under favorable circumstances.
关键词： Deformation measurement,Discontinuous displacement measurement,Digital image correlation,Reconstruction of displacement ?elds,DIC,Subset segmentation
更新于2025-09-04 15:30:14
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A Novel Edge-Map Creation Approach for Highly Accurate Pupil Localization in Unconstrained Infrared Iris Images
摘要： Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. The iris segmentation is a process of detecting the pupil, iris’s outer boundary, and eyelids in the iris image. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared (NIR) illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair. In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform (CHT) is used to find the pupil circle parameters in the edge-map. Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: CASIA-Iris-Thousand (version 4.0), CASIA-Iris-Lamp (version 3.0), and MMU (version 2.0). The average accuracy of the proposed method is 99.72% and average time cost per image is 0.727 sec.
关键词： pupil localization,iris segmentation,edge-map creation,non-close-up iris images,circular Hough transform,near-infrared illuminations
更新于2025-09-04 15:30:14
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Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer
摘要： Results of PET/CT examinations are communicated as text-based reports which are frequently not fully structured. Incomplete or missing staging information can be a significant source of staging and treatment errors. We compared standard text-based reports to a manual full 3D-segmentation-based approach with respect to TNM completeness and processing time. TNM information was extracted retrospectively from 395 reports. Moreover, the RIS time stamps of these reports were analyzed. 2995 lesions using a set of 41 classification labels (TNM features + location) were manually segmented on the corresponding image data. Information content and processing time of reports and segmentations were compared using descriptive statistics and modelling. The TNM/UICC stage was mentioned explicitly in only 6% (n=22) of the text-based reports. In 22% (n=86), information was incomplete, most frequently affecting T stage (19%, n=74), followed by N stage (6%, n=22) and M stage (2%, n=9). Full NSCLC-lesion segmentation required a median time of 13.3 min, while the median of the shortest estimator of the text-based reporting time (R1) was 18.1 min (p<0.001), lesion size (p<0.001) correlated significantly with the segmentation time, but not with the estimators of text-based reporting time. Numerous text-based reports are lacking staging information. A segmentation-based reporting approach tailored to the staging task improves report quality with manageable processing time and helps to avoid erroneous therapy decisions based on incomplete reports. Furthermore, segmented data may be used for multimedia enhancement and automatization.
关键词： segmentation,TNM staging,structured reporting,NSCLC,PET/CT
更新于2025-09-04 15:30:14