This study formulates the IR target detection as a binary classification problem of each pixel. Each pixel is associated with a label which indicates whether it is a target or background pixel. The optimal label set for all the pixels of an image maximizes aposteriori distribution of label configuration given the pixel intensities. The posterior probability is factored into (or proportional to)...

In recent years, the protection and management of water environments have garnered heightened attention due to their critical importance. Detection small objects in unmanned aerial vehicle (UAV) images remains a persistent challenge limited pixel values interference from background noise. To address this challenge, paper proposes an integrated object detection approach that utilizes improved YO...

Semiconductor pixel detectors offer features for the detection of radiation which are interesting for particle physics detectors as well as for imaging e.g. in biomedical applications (radiography, autoradiography, protein crystallography) or in Xray astronomy. At the present time hybrid pixel detectors are technologically mastered to a large extent and large scale particle detectors are being ...

Skin detection is the process of finding skin-colored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in images. Several computer vision approaches have been developed for skin detection. A skin detector typically transforms a given pixel into an appropriate color space and then use a s...

object detection plays an important role in successfulness of a wide range ofapplications that involve images as input data. in this paper we have presented anew approach for background modeling by nonconsecutive frames differencing.direction and velocity of moving objects have been extracted in order to get anappropriate sequence of frames to perform frame subtraction. stationary parts ofbackg...

Intelligent vehicle systems need to distinguish which objects are moving and which are static. A static concrete wall lying in the path of a vehicle should be treated differently than a truck moving in front of the vehicle. This paper proposes a new algorithm that addresses this problem, by providing dense dynamic depth information, while coping with real-time constraints. The algorithm models ...

Image processing in machine vision is a challenging task because often real-time requirements have to be met in these systems. To accelerate the processing tasks in machine vision and to reduce data transfer latencies, new architectures for embedded systems in intelligent cameras are required. Furthermore, innovative processing approaches are necessary to realize these architectures efficiently...

A number of pixel-based image fusion algorithms (using averaging, contrast pyramids, the discrete wavelet transform and the dualtree complex wavelet transform (DT-CWT) to perform fusion) are reviewed and compared with a novel region-based image fusion method which facilitates increased flexibility with the definition of a variety of fusion rules. The DT-CWT method could dissolve an image into s...

Two problems in computer vision are object classification and detection. Object classification is the determination of what category an object belongs to and object detection is the determination of where suspicious objects are in a large picture and what class they belong to. Given the advantageous of an automated recognition system, a solution to this problem has always been a desirable objec...

We propose a convolutional network with hierarchical classifiers for per-pixel semantic segmentation, which is able to be trained on multiple, heterogeneous datasets and exploit their semantic hierarchy. Our network is the first to be simultaneously trained on three different datasets from the intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and is able to handle differ...