Ismet Zeki Yalniz

Automatic Detection and Segmentation of Orchards Using Very High Resolution Imagery

Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data.

A Fast Alignment Scheme for Automatic OCR Evaluation of Books

This paper aims to evaluate the accuracy of optical character recognition (OCR) systems on real scanned books. The ground truth e-texts are obtained from the Project Gutenberg website and aligned with their corresponding OCR output using a fast recursive text alignment scheme (RETAS). First, unique words in the vocabulary of the book are aligned with unique words in the OCR output. This process is recursively applied to each text segment in between matching unique words until the text segments become very small. In the final stage, an edit distance based alignment algorithm is used to align these short chunks of texts to generate the final alignment. The proposed approach effectively segments the alignment problem into small sub problems which in turn yields dramatic time savings even when there are large pieces of inserted or deleted text and the OCR accuracy is poor. This approach is used to evaluate the OCR accuracy of real scanned books in English, French, German and Spanish.

An Efficient Framework for Searching Text in Noisy Document Images

An efficient word spotting framework is proposed to search text in scanned books. The proposed method allows one to search for words when optical character recognition (OCR) fails due to noise or for languages where there is no OCR. Given a query word image, the aim is to retrieve matching words in the book sorted by the similarity. In the offline stage, SIFT descriptors are extracted over the corner points of each word image. Those features are quantized into visual terms (visterms) using hierarchical K-Means algorithm and indexed using an inverted file. In the query resolution stage, the candidate matches are efficiently identified using the inverted index. These word images are then forwarded to the next stage where the configuration of visterms on the image plane are tested. Configuration matching is efficiently performed by projecting the visterms on the horizontal axis and searching for the Longest Common Subsequence (LCS) between the sequences of visterms. The proposed framework is tested on one English and two Telugu books. It is shown that the proposed method resolves a typical user query under 10 milliseconds providing very high retrieval accuracy (Mean Average Precision 0.93). The search accuracy for the English book is comparable to searching text in the high accuracy output of a commercial OCR engine.

Ottoman archives explorer: A retrieval system for digital Ottoman archives

This article presents Ottoman Archives Explorer, a Content-Based Retrieval (CBR) system based on character recognition for printed and handwritten historical documents. Several methods for character segmentation and recognition stages are investigated. In particular, sliding-window and histogram segmentation methods are coupled with recognition approaches using spatial features, neural networks, and a graph-based model. The prototype system provides CBR of document images using both example-based queries and a virtual keyboard to construct query words.

Unsupervised detection and localization of structural textures using projection profiles

The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.

Integrated segmentation and recognition of connected Ottoman script

We propose a novel context-sensitive segmentation and rec- ognition method for connected letters in Ottoman script. This method first extracts a set of segments from a connected script and determines the candidate letters to which extracted segments are most similar. Next, a function is defined for scoring each different syntactically correct se- quence of these candidate letters. To find the candidate letter sequence that maximizes the score function, a directed acyclic graph is con- structed. The letters are finally recognized by computing the longest path in this graph. Experiments using a collection of printed Ottoman docu- ments reveal that the proposed method provides 90% precision and recall figures in terms of character recognition. In a further set of experi- ments, we also demonstrate that the framework can be used as a build- ing block for an information retrieval system for digital Ottoman archives.

Creating an Improved Version Using Noisy OCR from Multiple Editions

This paper evaluates an automated scheme for aligning and combining optical character recognition (OCR) output from three scans of a book to generate a composite version with fewer OCR errors. While there has been some previous work on aligning multiple OCR versions of the same scan, the scheme introduced in this paper does not require that scans be from the same copy of the book, or even the same edition. The three OCR outputs are combined using an algorithm which builds upon an technique which aligns two sequences at a time. In the algorithm a multiple sequence alignment of the scans is generated by stitching together pair wise alignments and is used in turn to construct a corrected text. The approach is able to correct OCR errors so long as they do not occur in multiple scans. The proposed approach is shown to be effective even if some of the books contain additional content such as introductions or commentary. This scheme is used to generate improved versions from OCR texts taken from the Internet Archive. The accuracy of the original scans and the composite text are evaluated by comparing them to the version available from Project Gutenberg.

A framework for manipulating and searching multiple retrieval types

Conventional retrieval systems view documents as a unit and look at different retrieval types within a document. We introduce Proteus, a frame-work for seamlessly navigating books as dynamic collections which are defined on the fly. Proteus allows us to search various retrieval types. Navigable types include pages, books, named persons, locations, and pictures in a collection of books taken from the Internet Archive. The demonstration shows the value of multi-type browsing in dynamic collections to peruse new data.

Detecting regular plantation areas in satellite images

Detecting, segmenting and classifying agricultural fields in remote sensing images enable advanced planning of the land use economically. As most human structures, plants are cultivated in some order in orchards or farms. In this paper, a regularity detection method is proposed for exploiting this order information. The method slides windows over the spot filter responses of satellite images and analyzes their projection vectors. A regularity coefficient is calculated for each window. These regularity coefficients are further used for creating a regularity map, where regular regions obtain higher scores. These regularity maps can later be employed for the segmentation and classification of cultivation lands. The proposed method is illustrated in the detection of hazelnut orchards in sample high resolution satellite images.

Efficient Exploration of Text Regions in Natural Scene Images Using Adaptive Image Sampling

An adaptive image sampling framework is proposed for identifying text regions in natural scene images. A small fraction of the pixels actually correspond to text regions. It is desirable to eliminate non-text regions at the early stages of text detection. First, the image is sampled row-by-row at a specific rate and each row is tested for containing text using an 1D adaptation of the Maximally Stable Extremal Regions (MSER) algorithm. The surrounding rows of the image are recursively sampled at finer rates to fully contain the text. The adaptive sampling process is performed on the vertical dimension as well for the identified regions. The final output is a binary mask which can be used for text detection and/or recognition purposes. The experiments on the ICDAR’03 dataset show that the proposed approach is up to 7x faster than the MSER baseline on a single CPU core with comparable text localization scores. The approach is inherently parallelizable for further speed improvements.