The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.

Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold $h_{\mathrm{T}}$. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

This paper presents a novel method, Histogram of Angles in Linked Features (HALF), designed for the segmentation of 3D point cloud data of plants for robust sensing. The proposed method leverages local angular features extracted from 3D measurements obtained via sensing technologies such as laser scanning, LiDAR, or photogrammetry. HALF enables efficient identification of plant structures—leaves, stems, and knots—without requiring large-scale labeled datasets, making it highly suitable for applications in plant phenotyping and structural analysis. To enhance robustness and interpretability, we extend HALF to a convolution-based mathematical framework and introduce the Sequential Competitive Segmentation Algorithm (SCSA) for phytomer-level classification. Experimental results using 3D point cloud data of soybean plants demonstrate the feasibility of our method in sensor-based plant monitoring systems. By providing a low-cost and efficient approach for plant structure analysis, HALF contributes to the advancement of sensor-driven plant phenotyping and precision agriculture.

Hand-crumpled paper balls involve intricate structure with a network of creases and vertices, yet show simple scaling properties, which suggests self-similarity of the structure. We investigate the internal structure of crumpled papers by the microcomputed tomography (micro-CT) without destroying or unfolding them. From the reconstructed three-dimensional (3D) data, we examine several power laws for the crumpled square sheets of paper of the sizes $L=50$–300 mm and obtain the mass fractal dimension $D_M=2.7\pm 0.1$ by the relation between the mass and the radius of gyration of the balls and the fractal dimension $2.5\lesssim d_f\lesssim 2.8$ for the internal structure of each crumpled paper ball by the box counting method in the real space and the structure factors in the Fourier space. The data for the paper sheets are consistent with $D_M=d_f$, suggesting that the self-similarity in the structure of each crumpled ball gives rise to the similarity among the balls with different sizes. We also examine the cellophane sheets and the aluminium foils of the size $L=200$ mm and obtain $2.6\lesssim d_f\lesssim 2.8$ for both of them. The micro-CT also allows us to reconstruct 3D structure of a line drawn on the crumpled sheets of paper. The Hurst exponent for the root-mean-square displacement along the line is estimated as $H\approx 0.9$ for the length scale shorter than the scale of the radius of gyration, beyond which the line structure becomes more random with $H\sim 0.5$.

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.