Template Matching Scheme Research Articles

Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences

Optical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature point matching, few works have considered the registration issue for non-flat terrains, which will bring in more difficulties for not only sparse feature point matching but also outlier removal and geometric relationship estimation. This article addresses these issues with a novel and effective optical-SAR image registration framework. Firstly, sparse feature points are detected based on the phase congruency moment map of the textureless SAR image (SAR-PC-Moment), which helps to identify salient local regions. Then a template matching process using very large local image patches is conducted, which increases the matching accuracy by a significant margin. Secondly, a mutual verification-based initial outlier removal method is proposed, which takes advantage of the different mechanisms of sparse and dense matching and requires no geometric consistency assumption within the inliers. These two procedures will produce a putative correspondence feature point (CP) set with a low outlier ratio and high reliability. In the third step, the putative CPs are used to segment the large input image of non-flat terrain into dozens of locally flat areas using a recursive random sample consensus (RANSAC) method, with each locally flat area co-registered using an affine transformation. As for the mountainous areas with sharp elevation variations, anchor CPs are first identified, and then optical flow-based pixelwise dense matching is conducted. In the experimental section, ablation studies using four precisely co-registered optical-SAR image pairs of flat terrain quantitatively verify the effectiveness of the proposed SAR-PC-Moment-based feature point detector, big template matching strategy, and mutual verification-based outlier removal method. Registration results on four 1 m-resolution non-flat image pairs prove that the proposed framework is able to produce robust and quite accurate registration results.

An Image Stitching Scheme Based on Cameras Coordinate Transformation and Template Matching

In the detection of a large-size longitudinal beam workpiece, it is impossible to obtain the complete picture of the workpiece only by relying on a single camera, so it is necessary to realize the position movement of the camera with the help of a motor to obtain image data one by one, in which image stitching is the key to realize this technology. Based on this, this paper proposes an image stitching scheme based on the camera’s coordinate transformation and template matching. The scheme first uses a motor to drive three cameras to the image acquisition and then uses the coordinate transformation between cameras to obtain the stitched image of the area. After cameras are moved to the next image acquisition area, the image stitching algorithm based on template matching is used to complete the image stitching of the two areas. The above steps are repeated until the cameras complete the image acquisition task of all areas and the image stitching of the final workpiece is completed. Through the experimental verification, the proposed template matching scheme for the approximate overlapping areas between images can effectively improve the efficiency of image stitching, and the specific improvement effect in this experiment can reach 46.2%, and the overall image stitching scheme meets the requirements of detection efficiency.

Multi-pyramid-based hierarchical template matching for 6D pose estimation in industrial grasping task

PurposeTemplate matching is one of the most suitable choices for full six degrees of freedom pose estimation in many practical industrial applications. However, the increasing number of templates while dealing with a wide range of viewpoint changes results in a long runtime, which may not meet the real-time requirements. This paper aims to improve matching efficiency while maintaining sample resolution and matching accuracy.Design/methodology/approachA multi-pyramid-based hierarchical template matching strategy is proposed. Three pyramids are established at the sphere subdivision, radius and in-plane rotation levels during the offline template render stage. Then, a hierarchical template matching is performed from the highest to the lowest level in each pyramid, narrowing the global search space and expanding the local search space. The initial search parameters at the top level can be determined by the preprocessing of the YOLOv3 object detection network to further improve real-time performance.FindingsExperimental results show that this matching strategy takes only 100 ms under 100k templates without loss of accuracy, promising for real industrial applications. The authors further validated the approach by applying it to a real robot grasping task.Originality/valueThe matching framework in this paper improves the template matching efficiency by two orders of magnitude and is validated using a common template definition and viewpoint sampling methods. In addition, it can be easily adapted to other template definitions and viewpoint sampling methods.

Semantic maps for cross-view relocalization of terrestrial to UAV point clouds

• The semantic maps from point clouds favor re-localization. • Design rules to punish the predicted results for a novel template matching. • The proposed method can even address a single TLS scan to a global map. To generate a complete 3D point cloud model for complex urban environments, data must be captured from both top-view and street-view sides due to the limited field of view of the sensors. Unmanned aerial vehicle-laser scanning (UAV-LS) point clouds are captured from a top view, and they often contain information about building roofs and tree crowns. In contrast, terrestrial point clouds, collected by terrestrial laser scanners (TLS) and mobile laser scanners (MLS), are captured from a street view and contain information about building facades and tree stems. These two types of point clouds are challenging to register without manual efforts or target-based assistance because of (1) very limited overlapping regions caused by the cross-view and (2) the wide search of regions for the automatic localization of terrestrial scans in a large-scale scene from a UAV scan. Consequently, we highlight that a semantic map can be developed by utilizing the invariant semantic and geometric relationships to automatically locate from a local scan to a global scan. In our tasks, the buildings and tree regions are detected and assigned as semantic objects and input to generate a 2.5D semantic map related to their actual location information. We propose a novel template matching strategy combined with a penalty system to realize the localization. Specifically, the templates can be produced by the obtained semantic maps, and then the best localization can be achieved by a graded punishment method based on the developed rules. We emphasize that the proposed method can handle the integration of multiple-aligned scans and even a single scan by using a terrestrial scanner to a UAV scan owing to the stability and continuity of semantic information. In the experiments, we used a UAV to capture the lidar point clouds over the campus of the Southwest Jiaotong university and generated a global semantic map offline. Keeping the global map in hand, we collected the data from the street views by TLS and MLS also the campus and then implemented the localization from TLS/MLS point clouds to the UAV point clouds. The evaluations are grouped into two grades related to the degree of difficulty: (1) the regular group, which contains the multiple-registered TLS scans and MLS scans, and (2) the challenging group, which has independent TLS scans. In general, from the experimental results, for the proposed method, the success rate of localization is up to 100% in the regular group and 90% in the challenging group, indicating that only one in ten tests fails for the challenging group.

A Robust Strategy for Large-Size Optical and SAR Image Registration

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.

SFOC: A NOVEL MULTI-DIRECTIONAL AND MULTI-SCALE STRUCTURAL DESCRIPTOR FOR MULTIMODAL REMOTE SENSING IMAGE MATCHING

Abstract. Accurate matching of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, SAR, and rasterized maps) is still an ongoing challenge because of nonlinear radiometric differences (NRD) between these images. Considering that structural properties are preserved between multimodal images, this paper proposes a robust matching method based on multi-directional and multi-scale structural features, which consist of two critical steps. Firstly, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed to address severe NRD, which combines the first- and second-order gradient information by using the steerable filters to depict multi-directional and multi-scale structural features of images. Meanwhile, SFOC is further enhanced by performing the dilated Gaussian convolutions with different dilated rates on it, which can capture multi-level context structural features and improve the ability to resist noise. Then, a fast similarity measure, called Fast Normalized Cross-Correlation (Fast-NCCSFOC), is established to detect correspondences by a template matching scheme, which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. The performance of the proposed SFOC has been evaluated with many different kinds of multimodal RS images, and experimental results show its superior matching performance compared with the state-of-the-art methods.

A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Co-registration of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, and SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points (CPs) between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software (e.g., ENVI, ERDAS, and PCI) in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110/SFOC-Multimodal_Remote_Sensing_Image_Registration_System.

A Novel Heuristic Method for Linear Nearest Neighbour Realization of Reversible Circuits

With the advancement of technological footprints, high-end computation techniques, like quantum computation, have made significant progress in the design industry. Although quantum computation has proved itself as the next-generation computing paradigm, it is facing several design challenges, and one such design issue is in the form of the Nearest Neighbour (NN)t. This much-talked NN property demands the qubits in operating quantum gates to be adjacent, and a circuit should satisfy this NN property to be eligible for implementation. Considering the importance of NN enforcement in implementing circuits, we introduce a synthesis approach for the efficient transformation of reversible circuits to equivalent LNN representations. Our main focus in this work is to minimize the use of SWAP gates in designing NN-satisfied MCT-based reversible circuits. Towards transforming input circuits to LNN architecture, we have exploited a template matching scheme followed by two-qubit reordering policies, global and local, to improve the NN designs and reduce SWAP utilization. In finding the global solution, here we have used a dynamic programming algorithm where the initial order of qubit lines gets changed, while a heuristic-based SWAP placement function is used for finding a local qubit order sequence. The proposed synthesis workflow has been tested over an extensive set of benchmark functions, and comparison with the reported design approaches showed a substantial reduction in cost parameters.

A Fast Template Matching Scheme of Visible and Infrared Image Under Occluded Scenarios

A fast and robust template matching scheme, called Matching by Slice Transform Matrix Mapping (MSTMM), is proposed for the matching difficulty under occluded scenarios caused by nonlinear intensity differences and structure differences between visible and thermal infrared images. The first step in the MSTMM scheme was to extract information about the distribution of pixels with the same gray level through the developed Expanded Slice Transform with Adaptive Gray Level (EST-AGL). After completing the construction of the EST-AGL matrix for all image patches, different EST-AGL matrices were mapped to different integers or floats through the traditional special integer mapping mechanism or the neural network mapping mechanism. Finally, template matching between visible and thermal infrared images was achieved by evaluating the similarity of correlation mapping surface images through the Normalized Cross Correlation (NCC) algorithm. The proposed EST-AGL method can overcome the nonlinear intensity differences between visible and thermal infrared images by extracting the structural features of the image. The mapping mechanism of the MSTMM scheme can reduce the structural differences between the normal template image and the query image under an occluded scenario by increasing the similarity between the normal image patches and the image patches with occlusion. The proper mapping mechanism ensures the high performance of the MSTMM scheme by using only the simple NCC algorithm instead of other time-consuming anti-occlusion dense feature algorithms in the similarity evaluation stage. The three main experimental results of the MSTMM scheme are as follows: (1) the scheme of MSTMM can achieve template matching in only 0.015 seconds when a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$64\times 64$ </tex-math></inline-formula> template image slides on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$256\times 256$ </tex-math></inline-formula> query image on a hardware platform with limited resources; (2) the matching success rate of the MSTMM scheme can reach up to 75% among 2107 experimental samples; and (3) the neural network training in the neural network mapping mechanism only takes at least 104.4 seconds on the CPU.

The development of template-based facial expression perception from 6 to 15 years of age.

When perceiving emotional facial expressions, adults use a template-matching strategy, comparing the perceived face with a stored representation. A rejection of unnaturally exaggerated faces is characteristic of this strategy because the exaggerated expressions do not match the stored template. In contrast, a rule-based perceptual strategy (e.g., wide eyes indicate surprise) would be more tolerant of exaggeration. The current study uses exaggeration tolerance to test the expression perception strategies of children from 6 to 15 years of age. In Experiment 1, 62 (38 male) participants viewed pairs of happy or sad faces varying in exaggeration and selected the face that looked closest to how a happy (or sad) person really looks. With age, children became less likely to choose the more exaggerated expression. In Experiment 2, this result was replicated with each of the six basic emotions. Sixty-six children (26 male, 50 Caucasian, 10 mixed-race, four Indian, two unidentified) from 6 to 15 years of age completed the same experimental tasks as Experiment 1 for all six emotions. Again, with age children became less likely to choose the more exaggerated face. The results from both experiments suggest that the development of an adult-like template-matching strategy lasts into adolescence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Application of Transfer Learning Algorithm and Real Time Speech Detection in Music Education Platform

Artificial intelligence (AI), particularly machine learning (ML) and neural networks (NN), has various applications and has sparked a lot of interest in the recent years due to its superior performance in a variety of tasks. Automatic speech recognition (ASR) is a technique that is becoming more important with the passage of time and is being used in our daily lives. Speech recognition is an important application of ML and NN, which is the auditory system of machines that realize the communication between humans and machines. In general, speech recognition methods are divided into three types, i.e., based on the channel model and speech knowledge method, template matching scheme, and the use of NN method. The main problem associated with the existing speech recognition methods is the low recognition accuracy and more computation time. In order to overcome the problem of low recognition accuracy of existing speech recognition techniques, a speech recognition technology based on the combination of deep convolution neural network (DCNN) algorithm and transfer learning techniques, i.e., VGG-16, is proposed in this study. Due to the limited application range of DCNN, when the input and output parameters are changed, it is necessary to reconstruct the model that leads to a long training time of the architecture. Therefore, the migration learning method is conducive to reducing the size of the dataset. Various experiments have been performed using different dataset constructs. The simulation results show that transfer learning is not only suitable for the comparison between the source dataset and the target dataset, but also suitable for two different datasets. The application of small datasets not only reduces the time and cost of dataset generation, but also reduces the training time and the requirement of computing power. From the experimental results, it is quite obvious that the proposed system performed better than the existing speech recognition methods, and its performance is superior in terms of recognition accuracy than the other approaches.

Clifford+T-based implementation of fault-tolerant quantum circuits over XOR-Majority Graph

To build a scalable quantum computer that is sufficiently large to solve classically intractable problems is one of the biggest engineering quests of current times. But, the fragility of quantum states with short coherence time and vulnerability of quantum components towards external noise becomes an immense challenge to the physical implementation. Thus, the fault-tolerant architecture over the quantum error-correcting code (qecc) such as Surface code in optimal logical level are most desirable and can be achieved transversely using Clifford+T-group. However, the T-gate has high latency. In this paper, we introduce a template matching scheme over the Xor-Majority Graphs to realize a high scalable fault-tolerant quantum circuit. The proposed methodology is validated with multiple quantum benchmarks achieving an improvement of 71.98%, and 78.33% on average T−count, and T−depth respectively over state-of-art, and at the same time completely avoiding any ancillary input. When ancillary inputs are permitted, an improvement of 92.77% on T−depth is obtained.

Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector

Scanning nano-beam electron diffraction with a pixelated detector was employed to investigate the orientation relationship of nanometer sized, irradiation induced G-phase (M6Ni16Si7) precipitates in an austenite matrix. Using this detector, the faint diffraction spots originating from the small G-phase particles could be resolved simultaneously as the intense matrix reflections. The diffraction patterns were analyzed using a two-stage template matching scheme, whereby the matrix is indexed first and the precipitates are indexed second after subtraction of the matrix contribution to the diffraction patterns. The results show that G-phase forms with orientation relationships relative to austenite that are characteristic of face-centered cubic (FCC) to body-centered cubic (BCC) transformations. This work demonstrates that nano-beam electron diffraction with a pixelated detector is a promising technique to investigate orientation relationships of nano-sized precipitates with complex crystal structures in other material systems with relative ease.

Novel framework for automatic localisation of gun carrying by moving person using various indoor and outdoor mimic and real‐time views/Scenes

Hand held gun detection has an important application in both the field of video forensic and surveillance, because, gun is operative by hand only while committing any crime with it. The significant application encompasses the vulnerable places, such as around airport, marketplace, shopping malls, etc. In view of non-availability of relevant public data set, this study provides a newly created mimicked video data set for detection of gun carried by a person and entitled as Tripura University Video Data set for Crime-Scene-Analysis (TUVD-CSA). Effects of illumination, occlusion, rotation, pan, tilt, scaling of gun are effectively demonstrated in it. Moreover, the authors proposed an Iterative Model Generation Framework (IMGF) for gun detection, which is immune to scaling and rotation. Instead of locating the best matched object (gun) in the whole reference image to a query model via exhaustive search, IMGF searches only where the moving person carrying gun appears, which drastically reduces the computational overhead associated with a general template matching scheme. This has been employed by the background subtraction algorithm. Experimental results demonstrate that the proposed IMGF performs efficiently in gun detection with lesser number of true-negatives compared with the state-of-the-art methods.

Rotational invariant biologically inspired object recognition

The biologically inspired hierarchical model and X (HMAX) has been one of the superior techniques for object recognition purposes. HMAX is a robust method in the presence of some image variations including illumination, different scales, and location changes. However, the performance of HMAX extremely deteriorates if the orientation of the applied images in training phase is different than the orientation of testing images. In this study, the authors propose rotational invariant HMAX (RIMAX) to overcome the existing issues in object recognition imposed by rotations in the images. To this end, they embed two new layers into the structure of the standard HMAX. In the first added layer, non-accidental properties (e.g. corners and edges) are extracted as features that lead to obtaining a repeatable object recognition process. The second added layer provides robustness of RIMAX to image rotations by normalising the dominant orientation of the extracted features. Furthermore, they considerably reduce the imposed computational load by modifying template matching strategy as well as removing multiple scales of the Gabor filter. Simulation results employ Caltech101, TUD, Caltech5, and GRAZ-02 databases that validate RIMAX outperforms the standard HMAX and the other mathematical approaches in terms of robustness, accuracy, repeatability, and speed.

Decoding visual information from high-density diffuse optical tomography neuroimaging data

BackgroundNeural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated. ObjectiveTo assess the feasibility and performance of decoding with HD-DOT in visual cortex. Methods and ResultsTo establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs > 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing. ConclusionsVisual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations.

ROBUST MULTIMODAL IMAGE MATCHING BASED ON MAIN STRUCTURE FEATURE REPRESENTATION

Abstract. Image matching is a crucial procedure for multimodal remote sensing image processing. However, the performance of conventional methods is often degraded in matching multimodal images due to significant nonlinear intensity differences. To address this problem, this letter proposes a novel image feature representation named Main Structure with Histogram of Orientated Phase Congruency (M-HOPC). M-HOPC is able to precisely capture similar structure properties between multimodal images by reinforcing the main structure information for the construction of the phase congruency feature description. Specifically, each pixel of an image is assigned an independent weight for feature descriptor according to the main structure such as large contours and edges. Then M-HOPC is integrated as the similarity measure for correspondence detection by a template matching scheme. Three pairs of multimodal images including optical, LiDAR, and SAR data have been used to evaluate the proposed method. The results show that M-HOPC is robust to nonlinear intensity differences and achieves the superior matching performance compared with other state-of-the-art methods.

ROBUST REGISTRATION FOR OPTICAL AND SAR IMAGES BASED ON SPATIAL GEOMETRIC CONSTRAINT AND STRUCTURE FEATURES

Abstract. Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to significant geometric deformation and radiometric differences between two images. To address this issue, this paper proposes an automatic registration method for optical and SAR images based on spatial geometric constraint and structure features. Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the images. Subsequently, a local geometric correction is performed by using the Rational Function Model, which eliminates the rotation and scale differences between optical and SAR images. Secondly, orientated gradient information of images is used to construct a geometric structural feature descriptor. Then, the feature descriptor is transformed into the frequency domain, and the three-dimensional (3-D) phase correlation is used as the similarity metric to achieve correspondences by employing a template matching scheme. Finally, mismatches are eliminated based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Experimental results with multiple high-resolution optical and SAR images show that the proposed method can achieve reliable registration accuracy, and outperforms the state of the art methods.

Template matching route classification

AbstractThis paper details a route classification method for American football using a template matching scheme that is quick and does not require manual labeling. Pre-defined routes from a standard receiver route tree are aligned closely with game routes in order to determine the closest match. Based on a test game with manually labeled routes, the method achieves moderate success with an overall accuracy of 72% of the 232 routes labeled correctly.

A Very High-Speed Validation Scheme Based on Template Matching for Segmented Character Expiration Codes on Beverage Cans.

This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per second and validating up to 35 cans per second. This paper adds further detail to this description by describing the final stage of the MONICOD industrial validator: the process of effectively validating the characters. In this process we compare the acquired shapes, segmented during the prior stages, with expected character shapes. To do this, we use a template matching scheme (here called “morphologies”) based on bitwise operations. Two learning algorithms for building the valid morphology databases are also presented. The results of the study presented here show that in the acquisition of 9885 frames containing 465 cans to be validated, there was only one false positive (0.21% of the total). Another notable feature is that it is at least 20% faster in validation time with error rates similar to those of classifiers such as support vector machines (SVM), radial base functions (RBF), multi-layer perceptron with backpropagation (MLP) and k-nearest neighbours (KNN).