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Cameras record three color responses ($RGB$ ) which are device dependent. Camera coordinates are mapped to a standard color space, such as XYZ—useful for color measurement—by a mapping function, e.g., the simple $3\times 3$ linear transform (usually derived through regression). This mapping, which we will refer to as linear color correction (LCC), has been demonstrated to work well in the number...
We propose and verify a method for color-based cluster segmentation of the various tissues from ectocervix. That method uses a simplified compartment-like analysis, aiming for a Gaussian Mixture Model (GMM)-tailored segmentation. The tissues of interest are the cervical canal (CC), the columnar epithelium (CE), the squamous epithelium (SE) and the transformation zone (TZ) the latter known as area...
In this paper, we present a challenging dataset for the purpose of segmentation and change detection in photographic images of mountain habitats. We also propose a baseline algorithm for habitats segmentation to allow for performance comparison. The dataset consists of high resolution image pairs of historic and repeat photographs of mountain habitats acquired in the Canadian Rocky Mountains for ecological...
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are...
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View...
Limited size of object images and lack amount of training data would degrade the performance seriously in modern-day recognition applications. Therefore, how to effectively utilize available information from images becomes more and more important. In this paper, we propose to extend the linear regression classification (GLRC), which can effectively use all the information in cases of multiple inputs,...
Many attempts have been made to identify the region of interest in an image. In this paper, we have provided a new approach for ROI detection using the output of image annotation. Our claim is that because ROI is a subjective concept, a method should be used to diagnosis human mental models and for this purpose, we have used KNN base annotation in our method. Because many people in pictures that are...
In different color spaces, the three color channels might have different relationship, but most of color face recognition methods exploit the color information in a simple way. In this paper, we propose a novel hybrid fusion scheme for color face recognition, which first uses two-phase test sample representation (TPTSR) to obtain matching scores of each color channel of the test sample and then uses...
Visual information retrieval has become a major research area due to increasing rate at which images are generated in many application. This paper addresses an important problems related to the content-based images retrieval. It concerns the vector representation of images and its proper use in image retrieval. Indeed, we propose a new model of content-based image retrieval allowing to integrate theories...
People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: a) counting based on individual human detection; b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges,...
This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted...
During the last decades, several different techniques have been proposed for computer recognition of human faces. A further step in the development of these biometrics is to implement them in portable devices, such as mobile phones. Due to this devices' features and limitations it is necessary to select, among the currently available algorithms, the one with the best performance in terms of algorithm...
This paper describes the initial design of a computer vision application to recognize regulatory traffic signs vertically installed on Colombian roads using machine learning. This application is conceived as a module of a driver assistance system under development, and an autonomous vehicle adapted to the local infrastructure. The application was trained and tested with official synthetic images provided...
This paper presents a novel locally linear KNN method with an improved marginal Fisher analysis for image classification. First, the discriminating color space (DCS), which is derived by discriminant analysis of the red, green, and blue primary colors, is integrated into the proposed method. Second, an improved marginal Fisher analysis (IMFA) applies an eigenvalue spectrum analysis to improve the...
Classification of the content of a scanned document as either printed or handwritten is typically tackled as a segmentation problem of pages into text lines or words. However these methods are not applicable on documents where handwritten annotations overlay printed text. In this paper we propose to treat the task as a pixel classification task, i.e., To classify individual foreground pixels into...
Due to availability of reliable and low cost devices, range maps (depth maps) are extensively used in many applications. Recent advances in human-computer interaction enabled us to interact with computers in intuitive and friendly way. In this paper, we propose a novel approach for recognizing static hand gestures using depth information captured from Photon Mixing Device (PMD) cameras. We segment...
The goal of the project is to design intelligent and robust image-processing and augmented-reality algorithms for driver assistance and enhanced vehicular safety. In particular, the focuses were two-fold: (1) realizing the abilities to identify and localize in a vehicle''s on-board video the sweeping windshield wipers during raining days and (2) designing and implementing an in-painting technique...
A new hyper-spectral data set is at hand giving unique possibilities for investigating also multi-scale evidence fusion. In this contribution self-organizing maps are used for semi-supervised learning and visualization of the partially labeled data. The maps reveal that the seven classes given can be better distinguished using certain color and rotationally invariant texture features on the high-resolution...
Metric learning to learn a good distance metric for distinguishing different people while being insensitive to intra-person variations is widely applied to person re-identification. In previous works, local histograms are densely sampled to extract spatially localized information of each person image. The extracted local histograms are then concatenated into one vector that is used as an input of...
This paper focuses on the object recognition task and aims at improving the accuracy with an emphasis on the feature extraction step. Feature extraction is widely used in image classification as an initial step in the pipeline. In this paper, we propose a method to explore the conventional feature extraction techniques from the perspective that mid-level information could be incorporated in order...
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