This paper proposes an algorithm that enhances the contrast of input image using interpixel contextual information. The uses a 2-D histogram constructed mutual relationship between each pixel and its neighboring pixels. A smooth target is obtained by minimizing sum Frobenius norms differences from uniformly distributed histogram. enhancement achieved mapping diagonal elements to Experimental results show produces better or comparable enhanced images than four state-of-the-art algorithms.

In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in input image. The uses Gaussian mixture model to gray-level distribution, and intersection points of components are used partition dynamic range into intervals. equalized is generated by transforming pixels' gray levels each interval appropriate output according dominant component cumulative distribution function interval. To take account hypothesis homogeneous regions represent...

Mapping woody vegetation from aerial images is an important task bluein environment monitoring and management. A few studies have shown that semantic segmentation methods involving deep learning achieve significantly better performance in mapping than field-based measurement handcrafted features. However, current networks used for require labour-intensive pixel-level annotations. Thus, this paper proposes the use of image-level annotations a weakly supervised (WSSS) network based on Unmanned...

Emotion recognition using brain wave signals involves high dimensional electroencephalogram (EEG) data. In this paper, a window selection method based on mutual information is introduced to select an appropriate signal reduce the length of signals. The motivation windowing comes from EEG emotion being computationally costly and data having low signal-to-noise ratio. aim find reduced where emotions are strongest. it suggested, that only section which best describes improves classification...

Since remote sensing images of post-fire vegetation are characterized by high resolution, multiple interferences, and similarities between the background target area, it is difficult for existing methods to detect segment burned area in these with sufficient speed accuracy. In this paper, we apply Salient Object Detection (SOD) segmentation, first time has been done, propose an efficient segmentation network (BASNet) improve performance unmanned aerial vehicle (UAV) high-resolution image...

Accurate counting of maize tassels is essential for monitoring crop growth and estimating yield. Recently, deep-learning-based object detection methods have been used this purpose, where plant counts are estimated from the number bounding boxes detected. However, these suffer 2 issues: (a) The scales vary because image capture varying distances stage; (b) tassel areas tend to be affected by occlusions or complex backgrounds, making inefficient. In paper, we propose a multiscale lite...

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due data degradation caused by different conditions image acquisition, e.g., laboratory vs. field environment, machine learning-based models generated within a specific dataset (source domain) tend lose their validity when generalized novel (target domain). To this end, domain adaptation methods can be leveraged for the learning invariant representations across domains. In...

Existing view-invariant gait recognition methods encounter difficulties due to limited number of available views and varying conditions during training. This paper proposes partial similarity matching that assumes a 3D object shares common view surfaces in significantly different views. Detecting such aids the extraction features from multiple views; parametric body models are morphed by pose shape deformation template model using 2D silhouette as observation. The is estimated level set...

In this paper, we propose a novel unsupervised algorithm for segmenting sidescan sonar images of seafloor. The proposed does not make any priori assumption on the nature input image. first constructs multiresolution representation image using forward and inverse undecimated discrete wavelet transform (UDWT). A feature vector is then extracted each pixel both intra-resolution inter-resolution data. dimensionality reduced principal component analysis (PCA). vectors are clustered into disjoint...

Accurate counting of cereals crops, e.g., maize, rice, sorghum, and wheat, is crucial for estimating grain production ensuring food security. However, existing methods cereal crops focus predominantly on building models specific crop head; thus, they lack generalizability to different varieties. This paper presents Counting Heads Cereal Crops Net (CHCNet), which a unified model designed multiple heads by few-shot learning, effectively reduces labeling costs. Specifically, refined vision...

In this letter, a complex wavelet-domain image resolution enhancement algorithm based on the estimation of wavelet coefficients is proposed. The method uses forward and inverse dual-tree transform (DT-CWT) to construct high-resolution (HR) from given low-resolution (LR) image. HR reconstructed LR image, together with set coefficients, using DT-CWT. estimated DT-CWT decomposition rough Results are presented discussed very QuickBird data, through comparisons between state-of-the-art methods.