Isthmus critical points for solving jigsaw puzzles in computer vision

Webster, Roger; LaFollette, Paul S.; Stafford, Robert L.

doi:10.1109/21.120080

Cited by 46 publications

(19 citation statements)

References 17 publications

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“…Further, we note that several other papers on jigsaw assembly have thorough reviews of the related work, including [3,9,25]. We believe our paper is the first to deAuthor Year Color Shape Square Puzzle Pieces Size Freeman [7] 1964 9 Wolfson [22] 1988 104 Webster [21] 1991 9 Kosiba [13] 1994 54 Chung [4] 1998 54 Kong [12] 2001 32 Goldberg [9] 2002 204 Yao [25] 2003 12 Makridis [16] 2006 7 Sagiroglu [20] 2006 21 Nielsen [18] 2008 320 Alajlan [1] 2009 100 Cho [3] 2010 432 Yang [24] 2011 108 Pomeranz [19] 2011 3300 This work 2012 9600 Table 1: A selection of works on solving jigsaw puzzles, and whether image-based features or shape features are considered. Also, the size of the largest reconstructed puzzle (number of pieces) is given.…”

Section: Related Workmentioning

confidence: 99%

Jigsaw puzzles with pieces of unknown orientation

Gallagher

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

116

211

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Section: Related Workmentioning

confidence: 99%

Jigsaw puzzles with pieces of unknown orientation

Gallagher

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

116

211

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“…It is a method for general boundary curve representation and matching. Webster et al (1991) proposed isthmus critical points for solving jigsaw puzzles. Their solution deals with the canonical (off-the-shelf) jigsaw puzzles.…”

Section: Introductionmentioning

confidence: 99%

A shape and image merging technique to solve jigsaw puzzles

Yao

Shao

2003

Pattern Recognition Letters

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This paper proposes an algorithm for solving subsets of typical (canonical) jigsaw puzzles. This algorithm combines shape and image matching with a cyclic ''growth'' process that tries to place pieces in their correct positions. First, the jigsaw pieces are extracted from the input image. Then, the corner points of the jigsaw pieces are detected. Next, piece classification and recognition are performed based on jigsaw piece models. Connection relationships between pieces are calculated and finally recovered by using boundary shape matching and image merging. We tested this algorithm by employing real-world images containing dozens of jigsaw pieces. The experimentÕs results show this algorithm is efficient and effective.

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“…7. Due to the possible gaps between adjacent wall-painting fragments an alternative local curve matching procedure to the one described in [28], [29], [31], [34] etc., has been developed and is introduced in this paper.…”

mentioning

confidence: 99%

“…However, as we will point out below, the two problems solutions manifest drastic and essential differences. In fact, a number of papers deal with automatic puzzle solving: In [31] a set of critical points, the isthmus point and the isthmus critical points that define a feature used in matching partial boundaries is employed. A similar global feature of isthmus is used for automatic jigsaw puzzle solving in [32].…”

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confidence: 99%

Contour-shape based reconstruction of fragmented, 1600 BC wall paintings

Papaodysseus

Panagopoulos

Exarhos

et al. 2002

IEEE Trans. Signal Process.

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In this paper a novel general methodology is introduced for the computer-aided reconstruction of the magnificent wall-paintings of the Greek island Thera (Santorini), painted in the middle of the second millennium BC. These wall-paintings are excavated in fragments and, as a result, their reconstruction is a painstaking and a time-consuming process. Therefore, in order to facilitate and speed up this process a proper system has been developed based on the introduced methodology. According to this methodology each fragment is photographed, its picture is introduced to the computer, its contour is obtained and subsequently all fragments contours are compared in a manner proposed herein. Both the system and the methodology presented here, extract the maximum possible information from the contour shape of fragments of an arbitrary initially unbroken plane object, to point out possible fragments matching. This methodology has been applied to two excavated fragmented wall-paintings consisting of 262 fragments, with full success but most important it has been used to reconstruct, for the first time, unpublished wall-paintings parts from a set of 936 fragments. 2 A. INTRODUCTION-PROBLEM DESCRIPTIONThe discovery of the wall-paintings at Akrotiri of the Greek island Thera (Santorini), is of outstanding importance for human knowledge of the early Aegean world and not only. According to prominent archaeologists these wall-paintings rank alongside the greatest archaeological discoveries.The late professor Marinatos originated the excavations, which are now successfully continued by Professor Christos Doumas. As with the treasures of Pompeii and Herculaneum, the wall-paintings of Thera were preserved due to the seal of the pumice from the great eruption of a volcano [1]. As a rule, the walls decorated with paintings no longer survive. They collapsed together with their painted coat before the volcanic eruption, due to particularly strong earthquakes. Thus, a single painting is usually scattered into many fragments mixed with the fragments of other wall-paintings, too. The restoration of the wall-paintings from the fragments is a very painstaking and time consuming process frequently demanding many months or even years of dedicated, experienced personnel work for a single wallpainting restoration. Therefore, the development of a system that will contribute to the automatic restoration of these wall-paintings is of fundamental importance for this archaeological research, but for many others too, which face the problem of an image reconstruction from excavated fragments.Each excavated wall-painting fragment after being cleaned, is being photographed with a very strict protocol, so that very similar illumination conditions, a fixed distance of the fragment plane from the camera focus and minimal photo distortion are ensured. Subsequently, the obtained image is processed and eventually each photographed fragment is embedded into a white background frame, which we call the absolute frame of reference of the specific fragmen...

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Isthmus critical points for solving jigsaw puzzles in computer vision

Cited by 46 publications

References 17 publications

Jigsaw puzzles with pieces of unknown orientation

Jigsaw puzzles with pieces of unknown orientation

A shape and image merging technique to solve jigsaw puzzles

Contour-shape based reconstruction of fragmented, 1600 BC wall paintings

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