122,480 research outputs found
ORGAN LOCALIZATION AND DETECTION IN SOW’S USING MACHINE LEARNING AND DEEP LEARNING IN COMPUTER VISION
The objective of computer vision research is to endow computers with human-like perception to enable the capability to detect their surroundings, interpret the data they sense, take appropriate actions, and learn from their experiences to improve future performance. The area has progressed from using traditional pattern recognition and image processing technologies to advanced techniques in image understanding such as model-based and knowledge-based vision. In the past few years there has been a surge of interest in machine learning algorithms for computer vision-based applications. Machine learning technology has the potential to significantly contribute to the development of flexible and robust vision algorithms that will improve the performance of practical vision systems with a higher level of competence and greater generality. Additionally, the development of machine learning-based architectures has the potential to reduce system development time while simultaneously achieving the above-stated performance improvements. This work proposes the utilization of a computer vision-based approach that leverages machine and deep learning systems to aid the detection and identification of sow reproduction cycles by segmentation and object detection techniques. A lightweight machine learning system is proposed for object detection to address dataset collection issues in one of the most crucial and potentially lucrative farming applications. This technique was designed to detect the vulvae region in pre-estrous sows using a single thermal image. In the first experiment, the support vector machine (SVM) classifier was used after extracting features determined by 12 Gabor filters. The features are then concatenated with the features obtained from the Histogram of oriented gradients (HOG) to produce the results of the first experiment. In the second experiment, the number of distinct Gabor filters used was increased from 12 to 96. The system is trained on cropped image windows and uses the Gaussian pyramid technique to look for the vulva in the input image. The resulting process is shown to be lightweight, simple, and robust when applied to and evaluated on a large number of images. The results from extensive qualitative and quantitative testing experiments are included. The experimental results include false detection, missing detection and favorable detection rates. The results indicate state-of-the-art accuracy. Additionally, the project was expanded by utilizing the You Only Look Once (YOLO) deep learning Object Detection models for fast object detection. The results from object detection have been used to label images for segmentation. The bounding box from the detected area was systematically colored to achieve the segmented and labeled images. Then these segmented images are used as custom data to train U-Net segmentation. The first step involves building a machine learning model using Gabor filters and HOG for feature extraction and SVM for classification. The results discovered the deficiency of the model, therefore a second stage was suggested in which the dataset was trained using YOLOv3-dependent deep learning object detection. The resulting segmentation model is found to be the best choice to aid the process of vulva localization. Since the model depends on the original gray-scale image and the mask of the region of interest (ROI), a custom dataset containing these features was obtained, augmented, and used to train a U-Net segmentation model. The results of the final approach shows that the proposed system can segment sow\u27s vulva region even in low rank images and has an excellent performance efficiency. Furthermore, the resulting algorithm can be used to improve the automation of estrous detection by providing reliable ROI identification and segmentation and enabling beneficial temporal change detection and tracking in future efforts
Detection-by-Localization: Maintenance-Free Change Object Detector
Recent researches demonstrate that self-localization performance is a very
useful measure of likelihood-of-change (LoC) for change detection. In this
paper, this "detection-by-localization" scheme is studied in a novel
generalized task of object-level change detection. In our framework, a given
query image is segmented into object-level subimages (termed "scene parts"),
which are then converted to subimage-level pixel-wise LoC maps via the
detection-by-localization scheme. Our approach models a self-localization
system as a ranking function, outputting a ranked list of reference images,
without requiring relevance score. Thanks to this new setting, we can
generalize our approach to a broad class of self-localization systems. Our
ranking based self-localization model allows to fuse self-localization results
from different modalities via an unsupervised rank fusion derived from a field
of multi-modal information retrieval (MMR).Comment: 7 pages, 3 figures, Technical repor
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
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