Remote sensing images often suffer from different types of haze. Its presence significantly complicates remotely sensed image analysis that is crucial for monitoring of land state and precision agriculture. Currently existing remote sensing dehazing methods are designed for achromatic haze, but in cases such as smoke from fires or sandstorms, the haze may have its own pronounced coloration. In this paper we propose a new hazed image formation model that considers chromatic haze. Using this model we propose a new single image dehazing method CADCP that is based on color attenuation and dark channel priors. For quality assessment of the proposed method we generated a dataset of remotely sensed images with simulated chromatic haze. The generated dataset includes data with various haze spatial distribution and density. Quality evaluation results including qualitative and quantitative approaches demonstrated better results of the proposed method comparing with other existing methods.
The study is referred to a task of 2D data slope estimation. We consider the integral projections analysis technique and a common criterion of sum of squared values (SSV) for optimal angle detection. This criterion is dependent on the density of input data and for very sparse data its efficiency significantly decreases. We propose the alternative criteria – the sum of the inversed lengths (SIL) that preserves SSV characteristics for dense data but that is much more robust for sparse input. The experiments conducted on simulated and real datasets demonstrate better quality of slope detection using the proposed criterion.
In this work we discuss the task of search, localization and recognition of price zone within a photograph of the price tag. The task is being addressed for the case when image is acquired by small-scale digital camera and calculation device has significant resource constraints. The proposed approach is based on Niblack binarization algorithm, analysis and clasterization of connected components in conditions of known price tag geometrical model. The algorithm was tested on a private dataset and has shown high quality.
The growing adoption of intelligent transportation systems (ITS) and autonomous driving requires robust real-time solutions for various event and object detection problems. Most of real-world systems still cannot rely on computer vision algorithms and employ a wide range of costly additional hardware like LIDARs. In this paper we explore engineering challenges encountered in building a highly robust visual vehicle detection and classification module that works under broad range of environmental and road conditions. The resulting technology is competitive to traditional non-visual means of traffic monitoring. The main focus of the paper is on software and hardware architecture, algorithm selection and domain-specific heuristics that help the computer vision system avoid implausible answers.
We study the issue of performance improvement of classification-based object detectors by including certain geometric-oriented filters. Configurations of the observed 3D scene may be used as a priori or a posteriori information for object filtration. A priori information is used to select only those object parameters (size and position on image plane) that are in accordance with the scene, restricting implausible combinations of parameters. On the other hand the detection robustness can be enhanced by rejecting detection results using a posteriori information about 3D scene. For example, relative location of detected objects can be used as criteria for filtration. We have included proposed filters in object detection modules of two different industrial vision-based recognition systems and compared the resulting detection quality before detectors improving and after. Filtering with a priori information leads to significant decrease of detector's running time per frame and increase of number of correctly detected objects. Including filter based on a posteriori information leads to decrease of object detection false positive rate.
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