Discriminative Feature Co-Occurrence Selection for Object Detection

Mita, Takeshi; Kaneko, T.; Stenger, Björn; Hori, Osamu

doi:10.1109/tpami.2007.70767

Cited by 79 publications

(54 citation statements)

References 33 publications

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“…Yet, many recent methods have shown a remarkable success when are used in conjunction with machine learning techniques such as boosting [10,14,22,26,28] or Support Vector Machines (SVMs) [2,4,9,15,18]. However, these methods have been effectively used mostly for standard datasets [1,3,5,11] for which the objects only appear in a relatively reduced number of poses [7,20,24].…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Efficient rotation invariant object detection using boosted Random Ferns

Villamizar

Moreno-Noguer

Andrade‐Cetto

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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Section: Related Work and Contributionsmentioning

confidence: 99%

Efficient rotation invariant object detection using boosted Random Ferns

Villamizar

Moreno-Noguer

Andrade‐Cetto

et al. 2010

2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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“…Unfortunately, because of the packed single histogram, spatial relations among the LBPs are mostly discarded; thus, it results in the loss of global image information. Motivated by the co-occurrence concept such as in Co-HOG [29][30][31] and joint haar-like features [30], Nosaka et al [17,18] proposed to integrate context information (i.e., spatial and orientation) into the conventional LBP for achieving high descriptive capacity in image representation; the similar extension of LBP for integrating context information also can be seen in the recent work [19].…”

Section: Related Workmentioning

confidence: 99%

Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition

Han

Chen

2017

IPSJ T Comput Vis Appl

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In this paper, we describe a novel local descriptor of image texture representation for visual recognition. The image features based on micro-descriptors such as local binary patterns (LBP) and local ternary patterns (LTP) have been very successful in a number of applications including face recognition, object detection, and texture analysis. Instead of binary quantization in LBP, LTP thresholds the differential values between a focused pixel and its neighborhood pixels into three gray levels, which can be explained as the active status (i.e., positively activated, negatively activated, and not activated) of the neighborhood pixels compared to the focused pixel. However, regardless of the magnitude of the focused pixel, the thresholding strategy remains fixed, which would violate the principle of human perception. Therefore, in this study, we design LTP with a data-driven threshold according to Weber's law, a human perception principle; further, our approach incorporates the contexts of spatial and orientation co-occurrences (i.e., co-occurrence context) among adjacent Weber-based local ternary patterns (WLTPs, i.e., data-driven quantized LTPs) for texture representation. The explored WLTP is formulated by adaptively quantizing differential values between neighborhood pixels and the focused pixel as negative or positive stimuli if the normalized differential values are large; otherwise, the stimulus is set to 0. Our approach here is based on the fact that human perception of a distinguished pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. By integrating co-occurrence context information, we further propose a rotation invariant co-occurrence WLTP (RICWLTP) approach to be more discriminant for image representation. In order to validate the efficiency of our proposed strategy, we apply this to three different visual recognition applications including two texture datasets and one food image dataset and prove the promising performance that can be achieved compared with the state-of-the-art approaches.

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“…The gesture system works by integrating global hand detection [38,54] with local tracking. Users are able to control a screen pointer with their hand and select objects by moving their thumb.…”

Section: Commercial Gesture Interface Systemsmentioning

confidence: 99%

A Vision-Based Remote Control

Stenger

Woodley

Cipolla

2010

Studies in Computational Intelligence

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This chapter presents a vision-based system for touch-free interaction with a display at a distance. A single camera is fixed on top of the screen and is pointing towards the user. An attention mechanism allows the user to start the interaction and control a screen pointer by moving their hand in a fist pose directed at the camera. On-screen items can be chosen by a selection mechanism. Current sample applications include browsing video collections as well as viewing a gallery of 3D objects, which the user can rotate with their hand motion. We have included an up-to-date review of hand tracking methods, and comment on the merits and shortcomings of previous approaches. The proposed tracker uses multiple cues, appearance, color, and motion, for robustness. As the space of possible observation models is generally too large for exhaustive online search, we select models that are suitable for the particular tracking task at hand. During a training stage, various off-the-shelf trackers are evaluated. From this data different methods of fusing them online are investigated, including parallel and cascaded tracker evaluation. For the case of fist tracking, combining a small number of observers in a cascade results in an efficient algorithm that is used in our gesture interface. The system has been on public display at conferences where over a hundred users have engaged with it.

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Discriminative Feature Co-Occurrence Selection for Object Detection

Cited by 79 publications

References 33 publications

Efficient rotation invariant object detection using boosted Random Ferns

Efficient rotation invariant object detection using boosted Random Ferns

Co-occurrence context of the data-driven quantized local ternary patterns for visual recognition

A Vision-Based Remote Control

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