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105
FINE-GRAINED DYNAMIC VOLTAGE SCALING ON OLED DISPLAY FINE-GRAINED DYNAMIC VOLTAGE SCALING ON OLED DISPLAY
, 2010
"... iv Organic Light Emitting Diode (OLED) has emerged as a new generation of display techniques for mobile devices. Emitting light with organic fluorescent materials OLED display panels are thinner, brighter, lighter, cheaper and more power efficient, compared to other display technologies such as Liq ..."
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the color accuracy of the OLED pixels under scaled down supply voltage. Correspondingly, the OLED panel is partitioned into multiple display sections and each section's supply voltage is adaptively adjusted to implement fine-grained DVS with display content. When applied to display image, some
A 60fps 496mW Multi-Object Recognition Processor with Workload-Aware Dynamic Power Management
"... An energy efficient object recognition processor is proposed for real-time visual applications. Its energy efficiency is improved by lowering average power consumption while sustaining high frame rate. To this end, the proposed processor features from all levels of chip design. In architecture level ..."
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Cited by 1 (0 self)
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An energy efficient object recognition processor is proposed for real-time visual applications. Its energy efficiency is improved by lowering average power consumption while sustaining high frame rate. To this end, the proposed processor features from all levels of chip design. In architecture
Sparse output coding for large-scale visual recognition
- In CVPR
"... Abstract Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization ..."
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Cited by 2 (0 self)
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-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness
Sparse Output Coding for Large-Scale Visual Recognition
"... Many vision tasks require a multi-class classifier to dis-criminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a b ..."
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bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and prob-abilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
"... Abstract Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative examp ..."
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loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.
ABSTRACT Title of dissertation: LEARNING VISUAL PATTERNS: IMPOSING ORDER ON OBJECTS, TRAJECTORIES AND NETWORKS
"... Fundamental to many tasks in the field of computer vision, this work con-siders the understanding of observed visual patterns in static images and dynamic scenes. Within this broad domain, we focus on three particular subtasks, contribut-ing novel solutions to: (a) the subordinate categorization of ..."
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of subordinate cat-egorization rests on the ability to establish salient distinctions amongst the char-acteristics of those parts which comprise the basic-level category. Focusing on an avian domain due to the fine-grained structure of the category taxonomy, we ex-plore a pose-normalized appearance model based
Second Annual IEEE International Workshop on Horizontal Interactive Human-Computer System C-Slate: A Multi-Touch and Object Recognition System for Remote Collaboration using Horizontal Surfaces
"... We introduce C-Slate, a new vision-based system, which utilizes stereo cameras above a commercially available tablet technology to support remote collaboration. The horizontally mounted tablet provides the user with high resolution stylus input, which is augmented by multi-touch interaction and reco ..."
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and recognition of untagged everyday physical objects using new stereo vision and machine learning techniques. This provides a novel and interesting interactive tabletop arrangement, capable of supporting a variety of fluid multi-touch interactions, including symmetric and asymmetric bimanual input, coupled
S.: Video behavior profiling for anomaly detection
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2008
"... Abstract—This paper aims to address the problem of modeling video behavior captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling/detection without a ..."
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Cited by 57 (10 self)
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Abstract—This paper aims to address the problem of modeling video behavior captured in surveillance videos for the applications of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior profiling and online anomaly sampling/detection without
SHESHADRI ET AL.: EXEMPLAR DRIVEN CHARACTER RECOGNITION IN THE WILD 1 Exemplar Driven Character Recognition in the Wild
"... Character recognition in natural scenes continues to represent a formidable challenge in computer vision. Beyond variation in font, there exist difficulties in occlusion, background clutter, binarisation, and arbitrary skew. Recent advances have leveraged state of the art methods from generic object ..."
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object recognition to address some of these challenges. In this paper, we extend the focus to Indic script languages (e.g., Kannada) that contain large character sets (order of 1000 classes unlike 62 in English) with very low inter character variation. We identify this scenario as a fine grained visual
Face normalization using multi-scale cortical keypoints
"... Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extractions. Simple, complex and end- ..."
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Cited by 1 (1 self)
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-of-Attention can be explored to obtain face detection and normalization, after which face recognition can be achieved using the line/edge representation. In this paper, we focus only on face normalization, showing that multi-scale keypoints can be used to construct canonical representations of faces in memory. 1.
Results 1 - 10
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