# Object/Scene Recognition Based on a Directional Pixel Voting Descriptor
## 4 Results  
In this section, we present experimental results for the proposed algorithm compared with previous approaches. Since the use of standard datasets has become common in the computer vision area (for learning-based approaches), we looked for some proper datasets. However, since there is a lack of standardized datasets for image-based algorithms, previous works such as [13; 14; 27] used their own images, and this is the approach that we will follow here. Following this idea, we compare our algorithms with other canonical algorithms in the current literature, which have served as the basis for multiple works. These are SIFT [7], SURF [36], FAST [37], and ORB [9]. Multiple tests for object/scene detection and processing speed were carried out. For practical purposes, only some examples will be presented in this manuscript. In all cases, MatLab 2023b and a PC with an Intel(R) Core(TM) i9-9900K CPU 3.60 GHz, 32 GB RAM DDR3 were used (Intel, Santa Clara, CA, USA). In Figure 10, results for object detections are shown. In all cases, accuracy is defined as the percentage of template features successfully detected. Since accuracy is higher than 70%, the object being recognized is found in all cases. Different rotations and scale variations between the template and query were tested. In all cases, accurate detection can be reached. In Figure 9(a), two mismatches (outliers) were generated: in one case, the texture on the wall matched with a small pattern on the bottle. On the other hand, a line in the template was matched with a line in the query that is different from the line in the template. There is no global spatial information, which could be expected, since, following our descriptor, both lines' matches present a similar orientation, length, and gradient (as both present similar contrasts between background and object, of which the feature owns). Furthermore, we can see that the \"U\" character was found two times. This is because we have not applied any non-maximal matched, and therefore, one feature in the template can be matched multiple times in the query. In this case, each \"U\" character in the template can be matched with each \"U\" character in the query, as expected. In 10b,c, only one outlier was generated each. Finally, no outliers are presented in 10d. In all cases, the square/mesh pattern on the bottle generated mismatches, but again, given our algorithm undertakes feature matching based on shape and color contrast against the features (gradients), it is expected that repetitive patterns can generate wrong matches. These problems can be addressed by adding global information so that each feature can be the same in terms of gradient or shape but different, for example, with respect to the Euclidean distance to the origin. Of course, this is only a hypothesis and will be explored in future work. In any case, these results demonstrate, at a high level, relatively accurate detection for object recognition. Regarding performance under complex visual variations in In Figure 11, results for object detection are shown. In all cases, accuracy is defined as the percentage of template features that are successfully detected. Accuracy higher than 70% is reached, and therefore, accurate/robust detection is reached for objects. In Figure 11a, changes in rotation and objects with similar shapes were tested. In all cases, accurate detection can be reached. Even with rotations such as the one presented in the black cylinder and the wood box, our descriptor can properly match these objects. This demonstrates the rotational invariance of our descriptor. In Figure 11b, the complex texture scenario was tested, which demonstrated the robustness of the proposed method; shapes such as circles, cuboids, and so on and complex segments such as a book cover were correctly detected. Again, rotational invariance is demonstrated by the algorithm's ability to properly match segments inside the book cover.