scholarly journals Histograms of Pattern Sets for Image Classification and Object Recognition

2014 ◽  
Author(s):  
Winn Voravuthikunchai ◽  
Bruno Cremilleux ◽  
Frederic Jurie
Keyword(s):  
Object Recognition ◽  
Image Classification

scholarly journals SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shu Yang ◽  
JingWang ◽  
Sheeraz Arif ◽  
Minli Jia ◽  
Shunan Zhong
Keyword(s):  
Object Recognition ◽  
Image Classification ◽  
Semantic Segmentation ◽  
Experimental Results ◽  
Loss Functions ◽  
Learning Methods ◽  
Object Region ◽  
Triplet Loss ◽  
Attribute Learning ◽  
The Relationship

Existing attribute learning methods rely on predefined attributes, which require manual annotations. Due to the limitation of human experience, the predefined attributes are not capable enough of providing enough description. This paper proposes a self-supervised attribute learning (SAL) method, which automatically generates attribute descriptions by differentially occluding the object region to deal with the above problems. The relationship between attributes is formulated with triplet loss functions and is utilized to supervise the CNN. Attribute learning is used as an auxiliary task of a multitask image classification and segmentation network, in which self-supervision of attributes motivates the CNN to learn more discriminative features for the main semantic tasks. Experimental results on public benchmarks CUB-2011 and Pascal VOC show that the proposed SAL-Net can obtain more accurate classification and segmentation results without additional annotations. Moreover, the SAL-Net is embedded into a multiobject recognition and segmentation system, which realizes instance-aware semantic segmentation with the help of a region proposal algorithm and a fusion nonmaximum suppression algorithm.

scholarly journals Object Recognition Using Keras with Backend Tensor Flow

2018 ◽  
Vol 7 (3.6) ◽  
pp. 229
Author(s):  
Raswitha Bandi ◽  
J Amudhavel
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
Object Recognition ◽  
Image Classification ◽  
Deep Neural Networks ◽  
Human Beings ◽  
Recognition Of Objects

Now a day’s Machine Learning Plays an important role in computer vision, object recognition and image classification. Recognizing objects in images is an interesting thing, this recognization can be done easily by human beings but the computer cannot. The Problem with traditional neural networks is object recognition. So, to avoid difficulties in recognition of objects in images the deep neural networks especially Tensor flow under Keras Library is used and it will improve the Accuracy while recognizing objects. In this paper we present object recognition using Keras Library with backend Tensor flow. 

scholarly journals A scalable FPGA based accelerator for Tiny-YOLO-v2 using OpenCL

2019 ◽  
Vol 8 (3) ◽  
pp. 206
Author(s):  
Yap June Wai ◽  
Zulkanain Mohd Yussof ◽  
Sani Irwan Md Salim
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Object Detection ◽  
Image Classification ◽  
High Power ◽  
Energy Efficient ◽  
Detection Algorithm ◽  
Small Scale ◽  
Design Development ◽  
Deep Convolution Neural Network

Deep Convolution Neural Network (CNN) algorithm have recently gained popularity in many applications such as image classification, video analytic, object recognition and segmentation. Being compute-intensive and memory expensive, CNN computations are common accelerated by GPUs with high power dissipations. Recent studies show implementation of CNN on FPGA and it gain higher advantage in term of energy-efficient and flexibility over Software-configurable-GPUs. The proposed framework is verified by implement Tiny-YOLO-v2 on De1SoC. The design development in this project is HLS approach to ease effort from writing complex RTL codes and provide fast verification through emulation and profiling tools provided in the OpenCL SDK. To best of our knowledge, this is the first implementation of Tiny-YOLO-v2 CNN based object detection algorithm on a small scale De1SoC board using Intel FPGA SDK for OpenCL approach.

Use It and Still Lose It?

GeroPsych ◽  
2010 ◽  
Vol 23 (3) ◽  
pp. 169-175 ◽  
Author(s):  
Adrian Schwaninger ◽  
Diana Hardmeier ◽  
Judith Riegelnig ◽  
Mike Martin
Keyword(s):  
Object Recognition ◽  
Cognitive Development ◽  
Recognition Test ◽  
Cognitive Aging ◽  
Recognition Task ◽  
Airport Security ◽  
Visual Object ◽  
Long Term Effects ◽  
Object Recognition Test ◽  
X Ray

In recent years, research on cognitive aging increasingly has focused on the cognitive development across middle adulthood. However, little is still known about the long-term effects of intensive job-specific training of fluid intellectual abilities. In this study we examined the effects of age- and job-specific practice of cognitive abilities on detection performance in airport security x-ray screening. In Experiment 1 (N = 308; 24–65 years), we examined performance in the X-ray Object Recognition Test (ORT), a speeded visual object recognition task in which participants have to find dangerous items in x-ray images of passenger bags; and in Experiment 2 (N = 155; 20–61 years) in an on-the-job object recognition test frequently used in baggage screening. Results from both experiments show high performance in older adults and significant negative age correlations that cannot be overcome by more years of job-specific experience. We discuss the implications of our findings for theories of lifespan cognitive development and training concepts.

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