33,318 research outputs found
A CMOS image processing sensor for the detection of image features
A compact CMOS vision sensor for the detection of higher level image features, such as corners, junctions (T-, X-, Y-type) and linestops, is presented. The on-chip detection of these features significantly reduces the data amount and hence facilitates the subsequent processing of pattern recognition. The sensor performs a series of template matching operations in an analog/digital mixed mode for various kinds of image filtering operations including thinning, orientation decomposition, error correction, set operations, and others. The analog operations are done in the current domain. A design procedure, based on the formulation of the transistor mismatch, is applied to fulfill both accuracy and speed requirements. The architecture resembles a CNN-UM that can be programmed by a 30-bit word. The results of an experimental 16x16 pixel chip demonstrate that the sensor is able to detect features at high speed due to the pixel-parallel operation. Over 270 individual processing operations are performed in about 54 µsec
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
Various convolutional neural networks (CNNs) were developed recently that
achieved accuracy comparable with that of human beings in computer vision tasks
such as image recognition, object detection and tracking, etc. Most of these
networks, however, process one single frame of image at a time, and may not
fully utilize the temporal and contextual correlation typically present in
multiple channels of the same image or adjacent frames from a video, thus
limiting the achievable throughput. This limitation stems from the fact that
existing CNNs operate on deterministic numbers. In this paper, we propose a
novel statistical convolutional neural network (SCNN), which extends existing
CNN architectures but operates directly on correlated distributions rather than
deterministic numbers. By introducing a parameterized canonical model to model
correlated data and defining corresponding operations as required for CNN
training and inference, we show that SCNN can process multiple frames of
correlated images effectively, hence achieving significant speedup over
existing CNN models. We use a CNN based video object detection as an example to
illustrate the usefulness of the proposed SCNN as a general network model.
Experimental results show that even a non-optimized implementation of SCNN can
still achieve 178% speedup over existing CNNs with slight accuracy degradation.Comment: AAAI1
CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.Comment: Accepted at CVPR-201
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Over the past decade, deep neural networks (DNNs) have demonstrated
remarkable performance in a variety of applications. As we try to solve more
advanced problems, increasing demands for computing and power resources has
become inevitable. Spiking neural networks (SNNs) have attracted widespread
interest as the third-generation of neural networks due to their event-driven
and low-powered nature. SNNs, however, are difficult to train, mainly owing to
their complex dynamics of neurons and non-differentiable spike operations.
Furthermore, their applications have been limited to relatively simple tasks
such as image classification. In this study, we investigate the performance
degradation of SNNs in a more challenging regression problem (i.e., object
detection). Through our in-depth analysis, we introduce two novel methods:
channel-wise normalization and signed neuron with imbalanced threshold, both of
which provide fast and accurate information transmission for deep SNNs.
Consequently, we present a first spiked-based object detection model, called
Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable
results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial
datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic
chip consumes approximately 280 times less energy than Tiny YOLO and converges
2.3 to 4 times faster than previous SNN conversion methods.Comment: Accepted to AAAI 202
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