A geometric-based method for recognizing overlapping polygonal-shaped and semi-transparent particles in gray tone images

International audienceA geometric-based method is proposed to recognize the overlapping particles of different polygonal shapes such as rectangular, regular and/or irregular prismatic particles in a gray tone image. The first step consists in extracting the salient corners, identified by their locations and orientations, of the overlapping particles. Although there are certain difficulties like the perspective geometric projection, out of focus, transparency and superposition of the studied particles. Then, a new clustering technique is applied to detect the shape by grouping its correspondent salient corners according to the geometric properties of each shape. A simulation process is carried out for evaluating the performance of the proposed method. Then, it is particularly applied on a real application of batch cooling crystallization of the ammonium oxalate in pure water. The experimental results show that the method is efficient to recognize the overlapping particles of different shapes and sizes

Instance Flow Based Online Multiple Object Tracking

We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of segmented instances in subsequent frames by exploiting optical flow cues. We define an affinity matrix between instances of subsequent frames which reflects locality and visual similarity. The instance association is solved by applying the Hungarian method. We evaluate different configurations of our algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our tracking approach is able to track objects with high relative motions. In addition, we provide results of our approach on the MOT 2D 2015 test set for comparison with previous works. We achieve a MOTA score of 32.1

Convex Object Detection

Scene understanding has become a fundamental research area in computer vision. To achieve this task, localizing and recognizing different objects in space is paramount. From the seminal work of face detection by Viola-Jones using cascaded Haar features to the state-of-the-art deep neural networks, object detection has evolved from just being used in limited cases to be being used extensively for detecting common and custom objects. Algorithm and hardware improvements currently allow for real time object detection on a smartphone. Typically, for each detected object, the object top-left co-ordinate along with bottom-right coordinate or width and height are returned. While this works for objects whose boundaries are orthogonal to the image boundaries, it struggles to accurately localize rotated or non-rectangular objects. By regressing for eight corner points instead of the traditional of the top-left and bottom-right of a rectangular box, we can mitigate these problems. Building up from anchor-free one-stage object detection methods, it is shown that object detection can also be used for arbitrary shaped bounding boxes

Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it

Most computer vision application rely on algorithms finding local correspondences between different images. These algorithms detect and compare stable local invariant descriptors centered at scale-invariant keypoints. Because of the importance of the problem, new keypoint detectors and descriptors are constantly being proposed, each one claiming to perform better (or to be complementary) to the preceding ones. This raises the question of a fair comparison between very diverse methods. This evaluation has been mainly based on a repeatability criterion of the keypoints under a series of image perturbations (blur, illumination, noise, rotations, homotheties, homographies, etc). In this paper, we argue that the classic repeatability criterion is biased towards algorithms producing redundant overlapped detections. To compensate this bias, we propose a variant of the repeatability rate taking into account the descriptors overlap. We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors. Experimental evidence shows that the hierarchy of these feature detectors is severely disrupted by the amended comparator.Comment: Fixed typo in affiliation

Computing global shape measures

Global shape measures are a convenient way to describe regions. They are generally simple and efficient to extract, and provide an easy means for high level tasks such as classification as well as helping direct low-level computer vision processes such as segmentation. In this chapter a large selection of global shape measures (some from the standard literature as well as other newer methods) are described and demonstrated

Video alignment to a common reference

2015 Spring.Includes bibliographical references.Handheld videos often include unintentional motion (jitter) and intentional motion (pan and/or zoom). Human viewers prefer to see jitter removed, creating a smoothly moving camera. For video analysis, in contrast, aligning to a fixed stable background is sometimes preferable. This paper presents an algorithm that removes both forms of motion using a novel and efficient way of tracking background points while ignoring moving foreground points. The approach is related to image mosaicing, but the result is a video rather than an enlarged still image. It is also related to multiple object tracking approaches, but simpler since moving objects need not be explicitly tracked. The algorithm presented takes as input a video and returns one or several stabilized videos. Videos are broken into parts when the algorithm detects background change and it becomes necessary to fix upon a new background. We present two techniques in this thesis. One technique stabilizes the video with respect to the first available frame. Another technique stabilizes the videos with respect to a best frame. Our approach assumes the person holding the camera is standing in one place and that objects in motion do not dominate the image. Our algorithm performs better than previously published approaches when compared on 1,401 handheld videos from the recently released Point-and-Shoot Face Recognition Challenge (PASC)

SMOTE: Synthetic Minority Over-sampling Technique

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy