Patch Contour Matching by Correlating Fourier Descriptors

The Fourier descriptors (FDs) is a classical but still popular method for contour matching. The key idea is to apply the Fourier transform to a periodic representation of the contour, which results in a shape descriptor in the frequency domain. FDs are most commonly used to compare object silhouettes and object contours; the authors instead use this well-established machinery to describe local regions to be used in an object-recognition framework. Many approaches to matching FDs are based on the magnitude of each FD component, thus ignoring the information contained in the phase. Keeping the phase information requires us to take into account the global rotation of the contour and shifting of the contour samples. The authors show that the sum-of-squared differences of FDs can be computed without explicitly de-rotating the contours. The authors compare correlation-based matching against affine-invariant Fourier descriptors (AFDs) and WARP-matched FDs and demonstrate that correlation-based approach outperforms AFDs and WARP on real data. As a practical application the authors demonstrate the proposed correlation-based matching on a road sign recognition task.

Extremal Regions of Extremum Levels (EREL) are regions detected from a set of all extremal regions of an image. Maximally Stable Extremal Regions (MSER) which is a novel affine covariant region detector, detects regions from a same set of extremal regions as well. Although MSER results in regions with almost high repeatability, it is heavily dependent on the union-find approach which is a fairly complicated algorithm, and should be completed sequentially. Furthermore, it detects regions with low repeatability under the blur transformations. The reason for the latter shortcoming is the absence of boundaries information in stability criterion. To tackle these problems we propose to employ prior information about boundaries of regions, which results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of union-finding. To achieve that, we introduce Maxima of Gradient Magnitudes (MGMs) and use them to find handful of Extremum Levels (ELs). The chosen ELs are then scanned to detect their Extremal Regions (ER). The proposed algorithm which is called Extremal Regions of Extremum Levels (EREL) has been tested on the public benchmark dataset of Mikolajczyk [1]. Our experimental evaluations illustrate that, in many cases EREL achieves higher repeatability scores than MSER even for very low overlap errors.

Shape descriptors have demonstrated encouraging potential in retrieving images based on image content. A number of shape descriptors have been reported in the literature. Nevertheless, most of the reported descriptors still face accuracy and computational challenges. Fourier descriptors are considered to be promising descriptors as they are based on sound theoretical foundation, and possess computational efficiency and attractive invariance properties. In this paper, we propose a novel Fourier descriptor based on contour curvature. The proposed descriptor takes an unconventional view of the curvature-scale-space representation of a shape contour as it treats it as a 2-D binary image (hence referred to as Curvature-Scale Image, or CSI). The invariant descriptor is derived from the 2-D Fourier transform of the Curvature-Scale Image. This allows the descriptor to capture detailed dynamics of the shape curvature and enhance the efficiency of the shape matching process. Experiments using images from the MPEG-7 database have been conducted to compare the performance of the proposed descriptor with the Curvature- Scale-Space Descriptor (CSSD), the Generic Fourier Descriptor (GFD), and the 1-D Fourier Descriptor (1-FD). The proposed descriptor demonstrated superior performance.

The generation of text proposals (i.e. local candidate regions most likely containing textual components) is one critical and prerequisite step in scene text detection task. As one popular text proposal algorithm, the Maximally Stable Extremal Region (MSER), has been exploited by many successful text detection methods, while on the other hand has difficulties in handling complicated scene text involving touching characters and characters composed of multiple unconnected parts (e.g. Chinese characters and text in dot matrix fonts). In this paper, we propose a novel text proposal method for localizing text in natural images, which integrates the MSER algorithm with the multi-scale sliding window framework and efficiently extracts Windowed Maximally Stable Extremal Regions (WMSERs) as text proposals. We further present effective proposal filtering and grouping algorithms for exploiting WMSER-based proposals in text detection task. Experiments on public scene text datasets demonstrate the promising aspects of the proposed method in dealing with complicated scene text.

Local feature descriptors are widely used in many computer vision applications. Over the past couple of decades, several local feature descriptors have been proposed which are robust to challenging conditions. Since they show different characteristics in different environment, it is necessary to evaluate their performance in an intensive and consistent manner. However, there has been no relevant work that addresses this problem, especially for the affine invariant region detectors which are popularly used in object recognition and classification. In this paper, we present a useful and rigorous performance evaluation of local descriptors for affine invariant region detector, in which MSER (maximally stable extremal regions) detector is employed. We intensively evaluate local patch based descriptors as well as binary descriptors, including SIFT (scale invariant feature transform), SURF (speeded up robust features), BRIEF (binary robust independent elementary features), FREAK (fast retina keypoint), Shape descriptor, and LIOP (local intensity order pattern). Intensive evaluation on standard dataset shows that LIOP outperforms the other descriptors in terms of precision and recall metric.

In this paper a novel robust algorithm that extracts text from signage images is proposed. Existing methods are constructed to work on certain limitations, as detecting text under perfect lightning conditions. Maximally Stable Extremal Regions (MSER) detector is used to extract text areas because it shows better performance to lightning conditions. To enhance the MSER regions, novel enhancement technique is used by combining edge detection algorithm. Enhance edge image is then put to the stroke width detection algorithm. Based on stroke width information, connected components are obtained. Afterwards connected components are filtered to remove non text pixels. Text recognition is not touched by this algorithm and resulted binary image can be used on any text recognition algorithm. This algorithm shows best performance on light effected text images and noisy images.

Short-axis Cine Cardiac Magnetic Resonance Images (MRI) provide a good contrast between left ventricle (LV) blood pool and myocardium. Thus, they are suitable for segmenting left ventricle cavity and computing left ventricle areas, volumes and some important clinical parameters like ejection fraction and myocardial mass. Many recent endocardium segmentation methods utilize optimal thresholding as simple and fast technique to segment left ventricle cavity. Unfortunately, intensity inhomogeneity decreases the efficiency of optimal thresholding. In this paper, we propose an automatic technique to overcome the aforementioned problems. The proposed technique is based mainly on Maximally Stable Extremal Regions (MSER) detector. The proposed technique consists of two main steps: First, generating of MSERs and Second, these regions were analyzed to select the region representing the LV cavity. The proposed technique was validated using 15 test sets from MICCAI 2009 LV segmentation challenge. Results were compared with thresholding-based method applied to the same dataset. Results show better performance in terms of segmentation accuracy and computation time.

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

In this paper we introduce a novel real-time method to track weakly textured planar objects and to simultaneously estimate their 3D pose. The basic idea is to adapt the classic tracking-by-detection approach, which seeks for the object to be tracked independently in each frame, for tracking non-textured objects. In order to robustly estimate the 3D pose of such objects in each frame, we have to tackle three demanding problems. First, we need to find a stable representation of the object which is discriminable against the background and highly repetitive. Second, we have to robustly relocate this representation in every frame, also during considerable viewpoint changes. Finally, we have to estimate the pose from a single, closed object contour. Of course, all demands shall be accommodated at low computational costs and in real-time. To attack the above mentioned problems, we propose to exploit the properties of Maximally Stable Extremal Regions (MSERs) for detecting the required contours in an efficient manner and to apply random ferns as efficient and robust classifier for tracking. To estimate the 3D pose, we construct a perspectively invariant frame on the closed contour which is intrinsically provided by the extracted MSER. In our experiments we obtain robust tracking results with accurate poses on various challenging image sequences at a single requirement: One MSER used for tracking has to have at least one concavity that sufficiently deviates from its convex hull.

Decomposing Strain Maps Using Fourier-Zernike Shape Descriptors

This paper describes an object contour approximation method and its applications. It is assumed there is a rule R that locally estimates the boundaries of objects. Given a specified set of parameters, p1, ..., pn, and an index t (epsilon) {1, ..., M}, an object is abstracted as a closed curve described by a function C(p1, ..., pn, t) or simply C(t) where the other parameters are understood. An image strip centered at C(ti) and oriented along the normal to the curve at C(ti) is denoted by S(i). Boundary estimation further requires the specification of a function f(i), which is interpreted as a force applied to C(ti). F denotes the force vector (f(1), ..., f(m)). Given a initial curve C(p1(0), ..., pn(0), t), estimates for p1, ..., pn are refined iteratively by minimizing a nonnegative quantity (Epsilon) until pi(n) - 1) < (epsilon) for all i. In this paper, (Epsilon) takes the form: (omega) F2 + (1 - (omega) )F(infinity) , where the 2-norm favors point-group energy minimization fitting of the curve, and to (infinity) -norm favors individual point energy minimization fitting, and 0 &le; <i>w</i> &le; determines the relative importance of each type of fitting. A sequence of such curves initialized near an object will move and deform to fit the object and become fixed during the minimization process. Furthermore, we propose a method to describe the object contour when C(<i>t</i>) is stabilized. We also compare this method with the traditional curve fitting method, Fourier descriptors, dynamic splines, and deformable templates. Details are given for a simple form of this method using elliptical approximation. Finally, we present two applications of this method. The first one is for real-time object tracking, exploring the global shape. The other is for 2-D shape description using the feature vector once the global shape has been determined.

Accurate identification of fruits in greenhouse environments is an essential need for the precise functioning of agricultural robots. This study presents a solution to the problem of distinguishing cucumber fruits from their stems and leaves, which often have similar colors in their natural environment. The proposed algorithm for cucumber fruit identification relies on color segmentation and form matching. First, we get the boundary details from the acquired image of the cucumber sample. The edge information is described and reconstructed by utilizing a shape descriptor known as the Fourier descriptor in order to acquire a matching template image. Subsequently, we generate a multi-scale template by amalgamating computational and real-world data. The target image is subjected to color conditioning in order to enhance the segmenacktation of the target region inside the HSV color space. Then, the segmented target region is compared to the multi-scale template based on its shape. The method of color segmentation decreases the presence of unwanted information in the target image, hence improving the effectiveness of shape matching. An analysis was performed on a set of 200 cucumber photos that were obtained from the field. The findings indicate that the method presented in this study surpasses conventional recognition algorithms in terms of accuracy and efficiency, with a recognition rate of up to 86%. Moreover, the system has exceptional proficiency in identifying cucumber targets within greenhouses. This attribute renders it a great resource for offering technical assistance to agricultural robots that operate with accuracy.

Fourier descriptor is widely used for shape analysis and shape matching. Generally, the Euclid distance from boundary point to shape centroid is used in constructing Fourier descriptor. This kind of shape descriptor, however, is sensitive for rigid-transform. In this paper, we proposed a new kind of shape descriptor, namely geodesic Fourier descriptor. It remains robust under rigid transform. We first define a reference point by Poisson equation, which remains almost invariant under rigid transform. Then, the geodesic distance from shape boundary to reference point is used to construct GFD. Geodesic distance shows distinct advantage over the Euclid distance due to its robustness under rigid transformation. An algorithm based on two-scan dilating operation is presented to compute the geodesic distance efficiently in discrete image fields. Finally, experiments are carried out to show that geodesic Fourier descriptor can achieve better matching precision than Euclid distance based Fourier descriptor.

Selective Laser Melting (SLM) is a powder-bed additive manufacturing technique whose part quality depends critically on feedstock morphology. However, conventional powder characterization methods are low-throughput and qualitative, failing to capture the heterogeneity of industrial-scale batches. We present an automated, machine learning framework that couples high-throughput imaging with shape extraction and clustering to profile metallic powder morphology at scale. We evaluate three complementary approaches. The first uses an orientation-invariant variational autoencoder (VAE) to learn shape features that are insensitive to particle rotation, after which the resulting latent representations are grouped using clustering methods. The second applies classic shape descriptors – centroid distance functions, Fourier descriptors, and Zernike moments – followed by k-means or Gaussian mixture clustering to group particles with similar outlines. The third treats each particle’s radial profile as a continuous function and clusters these functional representations using the GPmix algorithm, allowing subtle differences in lobing and eccentricity to be captured. Across a dataset of approximately 126,000 powder images (0.5 – 102 μ m diameter), internal validity metrics identify the Fourier-descriptor + k-means pipeline as the most effective, achieving the lowest Davies–Bouldin index and highest Calinski–Harabasz score while maintaining sub-millisecond runtime per particle on a standard desktop workstation. The VAE provides orientation-invariant representations but at significantly higher computational cost, whereas the functional-data approach captures finer shape variations but requires a more complex configuration. Although the present work focuses on establishing the morphological-clustering framework, the resulting shape groups form a basis for future studies examining their relationship to flowability, packing density, and SLM part quality. Overall, this unsupervised learning framework enables rapid, automated assessment of powder morphology and supports tracking of shape evolution across reuse cycles, offering a path toward real-time feedstock monitoring in SLM workflows. • Automated machine learning framework for large-scale Selective Laser Melting (SLM) powder morphology profiling. • Evaluated three unsupervised pipelines: using variational autoencoder, shape descriptors, and functional clustering. • Fourier-descriptor + K-means achieved best accuracy, speed, and efficiency. • Framework distinguishes spherical, lobed, elongated, and defective particles. • Enables real-time monitoring of powder quality across reuse cycles in SLM.

Intraspecific variation in plant traits, such as leaf morphology, offers insights into local adaptation and the ecological niche breadth of species. Chenopodium hircinum, the wild ancestor of quinoa, is widely distributed across various ecoregions in Argentina. A detailed comparative analysis of leaf morphology across 23 populations covering its entire known distribution was conducted to understand patterns of intraspecific variation along geographic gradients. A total of 104 leaves from these populations were analysed using shape, landmarks, and Fourier descriptors. Significant variability in leaf shapes was observed across all ecoregions, with notable admixture among populations. Although landmark-based and Fourier analyses differentiated populations, shape descriptors added intricacy to identification. Leaf morphology ranged from rounded and toothed to sharply lobed, with lobes primarily located at the base or middle of the leaf. Key environmental factors influencing leaf shape were mean annual temperature (MAT) and altitude. The study suggests that leaf shape variability in C. hircinum extends beyond a simple lobed vs. entire dichotomy. Leaf morphology and margin traits are primarily governed by the relative position of the lobes, which correlate strongly with MAT at the population’s origin. This convergence across ecoregions highlights the need to explore the functional significance of this variation further.