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Mammogram images are now increasingly acquired with full-field digital mammography (FFDM) systems in the clinics. Traditionally, the “for-processing” format of FFDM images is used in computer-aided diagnosis (CAD) of breast cancer. In this study, we investigate the feasibility of using “for-presentation” format of FFDM (which are more readily available) in development of CAD algorithms for microcalcification...
In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or missed MCs in detection. To deal with difficulty,...
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as...
On the problem of tracking objects in videos, a recent and distinguished approach combining tracking and detection methods is the TLD framework. The detector identifies the object by its supposedly confirmed appearances. The tracker inserts new appearances into the model using apparent motion. Their outcomes are integrated by using the same similarity metric of the detector which, in our point of...
This paper presents an approach for visual tracking, consisting of two combination modules, which are global detector and local image patch matching. The former gives the classification response for each object candidate specified by the sliding window in the searching region. The classification can be performed by any global detector, which is based on the feature from the local patch in the object...
Starting from an object's location in a video frame, tracking-by-detection methods find the location of that object in a subsequent video frame. The tracker's detection step may produce multiple false positives during short-term occlusions, which can result in loss of track. We propose a tracking-by-detection method that is robust to short-term occlusions and false positives. Here, we extend the Struck...
When tracking multiple objects in an image sequence, various difficulties such as occlusion, mis-detection, false detection, and abrupt camera motion often occur together. Nevertheless, previous methods on multi-object tracking generally focus on only one or two of them. For that reason, the previous methods could not handle various problematic situations, where multiple difficulties occur simultaneously...
Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on sediment transport experiments in a flow were sediments are represented by spherical calibrated beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long...
Directly connected to the texture appearance, texture granularity is an effective measurement for geographic resources classification, product quality monitoring and image compression ratio selection. However, the application of existing works on texture granularity is limited by intense computation and the dependence on empirically selected parameters that vary among different textures. This paper...
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non-convex optimization to solve the latent variable. However, as the optimization objective is image-level instead of sample-level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same...
In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due to having large number of proposed regions...
Intrinsic natures of different appearance between sub-regions of objects and non-objects in optical flows lead to more visual consistency for object proposals. Hence, visual variations in different sub-regions in video sequences over time is a good indicator for likeliness of objects. We propose a method that dynamically measures the objectness of each proposal by exploiting temporal consistency within...
Computer vision algorithms are known to be extremely sensitive to the environmental conditions in which the data is captured, e.g., lighting conditions and target density. Tuning of parameters or choosing a completely new algorithm is often needed to achieve a certain performance level. In this paper, we focus on this problem and propose a framework to automatically choose the “best” algorithm-parameter...
A two stages car detection method using deformable part models with composite feature sets (DPM/CF) is proposed to recognize cars of various types and from multiple viewing angles. In the first stage, a HOG template is matched to detect the bounding box of the entire car of a certain type and viewed from a certain angle (called a t/a pair), which yields a region of interest (ROI). In the second stage,...
Fully autonomous navigation of unmanned vehicles, without relying on pre-installed tags or markers, still remains a challenge for GPS-denied areas and complex indoor environments. Doors are important for navigation as the entry/exit points. A novel approach is proposed to autonomously detect™ doorways by using the Project Tango platform. We first detect the candidate door openings from the 3D point...
Automatic object detection is a rapidly evolving area in surveillance and autonomous vehicles. Deformable part model (DPM) is a well-known object detector for its high precision and speed bottleneck. This paper proposes a very fast object detection pipeline based on complementary techniques to accelerate DPM. A recent fast feature pyramid technique is employed with look-up table HOG features, Fast...
The automatic detection of anomalies, defined as patterns that are not encountered in representative set of normal images, is an important problem in industrial control and biomedical applications. We have shown that this problem can be successfully addressed by the sparse representation of individual image patches using a dictionary learned from a large set of patches extracted from normal images...
Two sequential camera fingerprint detection methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of average number of test observations, is based on treating the...
This paper presents a fast algorithm for deriving the defocus map from a single image. Existing methods of defocus map estimation often include a pixel-level propagation step to spread the measured sparse defocus cues over the whole image. Since the pixel-level propagation step is time-consuming, we develop an effective method to obtain the whole-image defocus blur using oversegmentation and transductive...
This paper tackles the problem of bird detection in large landscape images for applications in the wind energy industry. While significant progress in image recognition has been made by deep convolutional neural networks (CNNs), small object detection remains a problem. To solve it, we follow the idea that a detector can be tuned to small objects of interest and semantic segmentation methods can be...
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