Developing Sidewalk Inventory Data Using Street View Images

(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street-level sidewalk detection method with image-processing Google Street View data. (2) Methods: Street view images were processed to produce graph-based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street-level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image-level sidewalk classifier had an 87% accuracy rate. The street-level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street-level sidewalk GIS data can be successfully developed using street view images.

Improved Content-Color-Dependent Screening (CCDS): Adaptive Bilateral Filtering and Color-Aware Sobel Edge Detector

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

Method for Improving an Image Segment in a Video Stream Using Median Filtering and Blind Deconvolution Based on Evolutionary Algorithms

Video surveillance systems, dash cameras and security systems have become an inescapable part of the most institutions ground environment. Their main purpose is to prevent incidents and to analyze the situation in case of extemporaneous events. Though as often as not it is necessary to increase an image segment many times over to investigate some incidents. Sometimes it is dozens of times. However, the obtained material is mostly of poor quality. This is connected either with noise or resolution characteristics, including focal distance. The paper considers an approach for improving image segments, which were obtained after multiple zooming. The main idea of the proposed solution is to use methods of blind deconvolution. In this case, the selection of restoration parameters is carried out using evolutionary algorithms with automatic evaluation of the result. That seems like the most important detail here is pre-processing besides noise minimization within the image, because when the image is repeatedly enlarged the effect of the noise component also increases. To avoid this thing, we suggest using ordinal statistics and average convolution for a series of images. The proposed solution was implemented as a software product, and its operation was tested on a number of video segments made under different shooting conditions. The results are presented at the end of this article.

Manajemen jpeg/exif file fingerprint dengan algoritma Brute Force string matching dan Hash Function SHA256

Management of jpeg/exif file fingerprint with Brute Force string matching algorithm and Hash Function SHA256Metode pengamanan berkas gambar jpeg/exif saat ini hanya mencakup aspek pencegahan, belum pada aspek deteksi integritas data. Digital Signature Algorithm (DSA) adalah metode kriptografi yang digunakan untuk memverifikasi integritas data menggunakan hash value. SHA256 merupakan hash function yang menghasilkan 256-bit hash value yang berfungsi sebagai file fingerprint. Penelitian ini bertujuan untuk menyusun file fingerprint dari berkas jpeg/exif menggunakan SHA256 dan algoritma Brute Force string matching untuk verifikasi integritas berkas jpeg/exif. Penelitian dilakukan dalam lima tahap. Tahap pertama adalah identifikasi struktur berkas jpeg/exif. Tahap kedua adalah akuisisi konten segmen. Tahap ketiga penghitungan hash value. Tahap keempat adalah eksperimen modifikasi berkas jpeg/exif. Tahap kelima adalah pemilihan elemen dan penyusunan file fingerprint. Hasil penelitian menunjukkan sebuah jpeg/exif file fingerprint tersusun atas tiga hash value. SOI (Start of Image) segment hash value digunakan untuk mendeteksi terjadinya modifikasi berkas dalam bentuk perubahan tipe berkas dan penambahan objek pada konten gambar. Hash value segmen APP1 digunakan untuk mendeteksi modifikasi pada metadata berkas. Hash value segmen SOF0 digunakan untuk mendeteksi gambar yang dimodifikasi dengan teknik recoloring, resizing, dan cropping. The method of securing jpeg/exif image files currently has covered only the prevention aspect instead of the data integrity detection aspect. Digital Signature Algorithm is a cryptographic method used to verify the data integrity using hash value. SHA256 is a hash function that produces a 256-bit hash value functioning as a fingerprint file. This study aimed at compiling fingerprint files from jpeg/exif files using SHA256 and Brute Force string matching algorithm to verify the integrity of jpeg/exif files. The research was conducted in five steps. The first step was identifying the jpeg/exif file structure. The second step was the acquisition of the segment content. The third step was calculating the hash value. The fourth step was the jpeg/exif file modification experiment. The fifth step was the selection of elements and compilation of fingerprint files. The obtained results showed a jpeg/exif fingerprint file which was compiled in three hash values. The hash value of SOI segment was used to detect the occurrence of file modification in the form of file type changing and object addition on the image content. The hash value of APP1 segment was used to detect the metadata file modification. The hash value of SOF0 segment was used to detect the images modified by recoloring, resizing, and cropping techniques.