Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses

Wu, Bo; Nevatia, Ram

doi:10.1007/s11263-008-0194-9

Cited by 135 publications

(68 citation statements)

References 46 publications

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“…Therefore, here we discuss automated learning-based object segmentation approaches, which might be adaptable to leaf segmentation. Wu and Nevatia [58] present an approach that detects and segments multiple, partially occluded objects in images, relying on a learned, boosted whole-object segmentor and several part detectors. Given a new image, pixels showing part responses are extracted and a joint likelihood estimation inclusive of inter-object occlusion reasoning is maximized to obtain final segmentations.…”

Section: Related Workmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

234

181

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Image-based plant phenotyping is a growing application domain of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first of its kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

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Section: Related Workmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

234

181

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“…multi-hypothesis detection problems [9,10,7,8], without restricting to [4]. Then we derive continuous valued features (e.g., {Dist j , P robDecay j }) more precisely describing the underlying "candidate-ICV" associations, which permits further statistical analysis and classification, converting from binary detections.…”

Section: Discussionmentioning

confidence: 99%

“…The "anchor-linking" multiple detection fusion is related to component based object detection methods [9,10], but different in maximizing the trustful object region recovery by linking a few spatially correlated, "strong" detection candidates, while [9,10] aggregate multiple part-based detections to form the whole-object identification. The feature extraction and classification treatment from detection, enables more rigorous statistical analysis and removes about 90% more ICV type (polyp) FPs (0.38 versus 0.2 per volume, due to ICV existence) than improved N-box detection (n=3).…”

Section: Introductionmentioning

confidence: 99%

Correcting Misalignment of Automatic 3D Detection by Classification: Ileo-Cecal Valve False Positive Reduction in CT Colonography

Lü

Wolf

et al. 2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. Ileo-Cecal Valve (ICV) is an important small soft organ which appears in human abdomen CT scans and connects colon and small intestine. Automated detection of ICV is of great clinical value for removing false positive (FP) findings in computer aided diagnosis (CAD) of colon cancers using CT colongraphy (CTC) [1][2][3]. However full 3D object detection, especially for small objects with large shape and pose variations as ICV, is very challenging. The final spatial detection accuracy often trades for robustness to find instances under variable conditions [4].In this paper, we describe two significant post-parsing processes after the normal procedure of object (e.g., ICV) detection [4], to probabilistically interpret multiple hypotheses detections. It achieves nearly 300% performance improvement on (polyp detection) FP removal rate of [4], with about 1% extra computional overhead. First, a new multiple detection spatial-fusion method utilizes the initial single detection as an anchor identity and iteratively integrates other "trustful" detections by maximizing their spatial gains (if included) in a linkage. The ICV detection output is thus a set of N spatially connected boxes instead of a single box as top candidate, which shows to correct 3D detection misalignment inaccuracy. Next, we infer the spatial relationship between CAD generated polyp candidates and the detected ICV bounding boxes in 3D volume, and convert as a set of continuous valued, ICV-association features per candidate which allows further statistical analysis and classification for more rigorous false positive deduction in colon CAD.Based on our annotated 116 training cases, the spatial coverage ratio between the new N-box ICV detection and annotation is improved by 13.0% (N=2) and 19.6% (N=3) respectively. An evaluation on large scale datasets of total ∼ 1400 CTC volumes, with different tagging preparations, reports average 5.1 FP candidates are removed at CandidateGeneration stage per scan; and the final CAD system mean FP rate drops from 2.2 to 1.82 per volume, without affecting the sensitivity.

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“…Opelt et al also compared each boundary fragment from each category to all existing alphabet entries using chamfer distance in [8]. Other methods that utilize chamfer distance as shape similarity metric include [9,13,20]. Chamfer distance plays also an important role in medical image analysis, e.g., [10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Boosting Chamfer Matching by Learning Chamfer Distance Normalization

Yang

Latecki

2010

Computer Vision – ECCV 2010

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Abstract. We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.

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Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses

Cited by 135 publications

References 46 publications

Leaf segmentation in plant phenotyping: a collation study

Leaf segmentation in plant phenotyping: a collation study

Correcting Misalignment of Automatic 3D Detection by Classification: Ileo-Cecal Valve False Positive Reduction in CT Colonography

Boosting Chamfer Matching by Learning Chamfer Distance Normalization

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