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In this paper we present a new distributed technique to geometrically calibrate multiple casually aligned projectors on a fiducial free arbitrary surface using multiple casually aligned cameras where every point of the surface is seen by at least one camera. Using a multi-step method that uses binary blob patterns, we estimate robustly the display's 3D surface geometry, the cameras' extrinsic parameters,...
Our research objective is to develop a supervised learning based hierarchical classification framework built upon Gabor features. Specifically, we experimented on the Oliva Tor alba data-set from the Corel stock photo library. This data set consists of 2688 natural and artificial scene color images, of size (256X256X3) each, from 8 sub-categories. In this paper, we restrict our goal to categorization...
This paper describes methods to evaluate (and train) pixel classifiers when connected components is used as a post-processing step. In previous work the method was used to train a convolutional neural network for image segmentation and we provided pseudo-code for a disjoint-set based algorithm that efficiently calculates the Rand Error and its gradient. This paper describes the modifications we have...
Spectroscopy, with the current 2D sensors, has been the study of the 4-dimension function F(x; y; t; l) that defines the intensity of light at the sensor location (x; y) at time t and at wavelength l. The goal of video rate spectroscopy is to capture, process, and display this function at reasonable discretized resolutions of each of the parameters x, y, t and l. Spectral imaging till date has been...
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task...
Hand shape recognition is a challenging task because hands are deformable objects. Some techniques for hand shape recognition using Computer Vision have been proposed. The key problem is how to make hand gestures understood by computers/mobile devices. In this paper we present a study about Principal Component Analysis (PCA) used to reduce the dimensionality and extract features of images of the human...
Videos from an Unmanned Aerial Vehicle (UAV) platform have become more available in the surveillance community. As the number of video samples are increasing, automated analysis of these videos have become important. In this paper, we describe the task of vehicle movement event classification in videos taken from UAV. Given object track information from UAV video sequences, we processed the noisy...
In recent years, video timing characteristics of facial expression recognition research has become a hot topic. In this article, through analyzing changes in the feature data in realtime detection of facial expression, and combining temporal and spatial features to establish models, we put forward a more rapid and more efficient method to recognize facial expressions. Firstly, the feature extraction...
Billions of geotagged ground-level images are available via social networks and Google Street View. Recent work in computer vision has explored how these images could serve as a resource for understanding our world. However, most ground-level images are captured in cities and around famous landmarks; there are still very large geographic regions with few images. This leads to artifacts when estimating...
Traffic light detection is an important part of Advanced Driver Assist as well as autonomous vehicle systems which ensures timely and appropriate reaction to traffic lights (TLs) in cross sections. In this paper we introduce a robust and realtime approach to detect TLs and recognize its status in complex traffic scenes solely based on image processing techniques. The proposed system uses color properties...
Visualizing and working with large scale scientific image data can benefit greatly from advances in consumer Virtual Reality and game development tools, but presently only limited applications have leveraged these. We present a set of techniques for mapping scientific images and data manipulation tasks into virtual reality applications with an emphasis on short development time and high quality user...
Screening mammography often incorporates a computer aided diagnosis (CAD) scheme in its procedure to increase the detection rates of gradual changes in breast tissues. One method for detecting gradual changes in temporal mammograms is through the use of registration algorithms. Images of degraded resolution present an obstacle to accurate registration, however. The performance of registration algorithms...
The ability to accurately detect and classify objects at varying pixel sizes in cluttered scenes is crucial to many Navy applications. However, detection performance of existing state-of-the-art approaches such as convolutional neural networks (CNNs) degrade and suffer when applied to such cluttered and multi-object detection tasks. We conjecture that spatial relationships between objects in an image...
Fast Fourier Transforms have been used since the early 1960s as a method of processing signals. Since the 1990s wavelet transformation has also been routinely used as method for signal processing. Their limitations include the inability to detect contours, curves and directional information of a signal. In the past few years, new approaches such as multi-scale and multi-resolution transformations...
Image analysis is essential through a wide range of scientific areas and most of them have one task in common, i.e. object detection. Thus automated detection algorithms had generated a lot of interest. This proposal identifies objects with similar features on a frame. The inputs are the image where to look at, and a single appearance of the object we are looking for. The object is searched by a sliding...
Recent changes to the topology of regional convolutional neural networks (rCNN) have allowed them to obtain near real-time speeds in image detection. We propose a method for region proposal alternate to selective search which is used in the current state of the art object detection [3] and introduce the fine grained image datasets. In a maritime surveillance setting, it may be important to not only...
Motion estimation from video is an increasingly important problem with applications in ego-motion estimation of an unmanned vehicle, segmentation from video, object detection and tracking, and many others. Recent advances in optical flow have made motion estimation possible in many applications with high-resolution imagery. However, in the presence of noise and compression artifacts, these state-of-the-art...
In image classification tasks, the image is rarely represented as only a collection of raw pixels. Myriad alternative representations, from Gaussian kernels to bags-of-words to layers of a convolutional neural network, have been proposed both to decrease the dimensionality of the task and, more importantly, to move into a space which better facilitates classification. This work explores several methods...
To design robust Pre-Collision Systems (PCS) we must develop new techniques that will allow a better understanding of the vehicle-pedestrian dynamic relationship, and which can predict pedestrian future movements. This paper focuses on the potential-conflict situations where a collision may happen if no avoidance action is taken from driver or pedestrian. We have used 1000 15-second videos to find...
In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better...
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